On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

Trend · papers per month

count % of corpus

48 results for “feature kernel”

Kernel Subspace and Feature Extractioncs.LG

Kernel methods linked to feature subspaces and maximal correlation kernels.

problem Understanding kernel methods and their relationship to feature extraction.

method Established a correspondence between feature subspaces and kernels, introduced maximal correlation kernels, and demonstrated their optimality.

result Kernel SVM on maximal correlation kernel achieves minimum prediction error.

kernel methods feature extraction maximal correlation support vector machine information theory

2023-01-04abs ↗pdf ↗

Orthogonal Random Features: Explicit Forms and Sharp Inequalitiescs.LG

Orthogonal random features approximate a Bessel kernel, offering sharper bounds than random Fourier features.

problem Approximating Gaussian kernel efficiently for large datasets.

method Use of Haar orthogonal matrices to construct orthogonal random features and analyze their bias and variance.

result Orthogonal random features approximate a Bessel kernel, not the Gaussian kernel, with sharper bounds.

Random Features Kernel Approximation Orthogonal Matrices Bessel Functions Gaussian Kernel

2023-10-11abs ↗pdf ↗

Feature maps for the Laplacian kernel and its generalizationsstat.ML

New random feature maps for Laplacian and related kernels.

problem Challenges in approximating the Laplacian kernel and its generalizations.

method Developed random feature maps for Laplacian and related kernels, providing efficient sampling schemes.

result Demonstrated the efficacy of these random feature maps on real datasets.

kernel methods Laplacian kernel Matérn kernel Exponential power kernel random Fourier features

2025-02-21abs ↗pdf ↗

Fair Kernel Regression via Fair Feature Embedding in Kernel Spacecs.LG

Paper proposes FKR-F $^2$ E to make kernel regression fair.

problem Mitigating demographic biases in kernel methods.

method Fair feature embedding in kernel space.

result FKR-F

^2

E achieves lower prediction disparity.

kernel regression fairness demographic discrepancy feature embedding

2019-07-04abs ↗pdf ↗

On the expressivity of embedding quantum kernelsquant-ph

Quantum kernels can be efficiently embedded into classical feature spaces.

problem Can all quantum kernels be efficiently embedded into classical feature spaces?

method Invoking computational universality and using techniques like random Fourier features, the authors show that certain classes of quantum kernels can be efficiently embedded.

result For shift-invariant and composition kernels, embedding quantum kernels are universal and efficient.

quantum kernels embedding kernel methods computational universality random Fourier features

2023-09-25abs ↗pdf ↗

Taming Nonconvexity in Kernel Feature Selection -- Favorable Properties of the Laplace Kernelmath.ST

Laplace kernel feature selection offers statistical guarantees for nonparametric models with few samples.

problem Statistical guarantees for kernel-based feature selection in nonconvex optimization problems.

method Sharp characterization of the gradient of the objective function for Laplace kernel feature selection.

result Model-selection consistency for Laplace kernel-based feature selection in nonparametric settings with

n \sim \log p

samples.

kernel feature selection nonconvex optimization Laplace kernel model selection statistical guarantees

2021-06-17abs ↗pdf ↗

Fast Graph Kernel with Optical Random Featurescs.LG

A faster graph kernel using optical random features.

problem High computation cost of graphlet kernel due to isomorphism test.

method Kernel random features, optical random features, mean kernel metric.

result The proposed method is orders of magnitude faster with similar or better accuracy.

graph classification graphlet kernel kernel random features optical random features mean kernel metric

2020-10-16abs ↗pdf ↗

Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Featurescs.LG

Paper develops a new kernel expansion method using entropic optimal features for sparse and efficient kernel approximation.

problem Efficient kernel approximation with reduced computational cost and feature dissimilarity.

method Develops a novel optimal design maximizing entropy among kernel features, resulting in a sparse kernel expansion.

result Achieves optimal statistical accuracy with only $O(N^{rac{1}{4}})$ features, significantly reducing time and space costs.

Kernel approximation Sparse features Entropy maximization Optimal design Generalization bound

2020-02-11abs ↗pdf ↗

Improved Random Features for Dot Product Kernelsstat.ML

Improves efficiency of random feature approximations for dot product kernels.

problem Efficiency of random feature approximations for dot product kernels.

method Generalization of existing random feature approximations using complex-valued random features, theoretical analysis of variances, data-driven optimization approach.

result Complex-valued random features can significantly reduce the variances of approximations.

random features dot product kernels machine learning kernel methods variance reduction

2022-01-21abs ↗pdf ↗

Scaling up Kernel Ridge Regression via Locality Sensitive Hashingstat.ML

Improved kernel ridge regression for large datasets using weighted random binning.

problem Efficiently approximating kernel matrices for large-scale datasets.

method Introduced weighted random binning features for locality sensitive hashing.

result Weighted random binning features generate Gaussian processes of any desired smoothness.

Kernel Ridge Regression Locality Sensitive Hashing Random Binning Features Gaussian Processes Kernel Approximation

2020-03-21abs ↗pdf ↗

KNIFE: Kernel Iterative Feature Extractionstat.ML

Selecting important features in non-linear or kernel spaces is a difficult challenge in both classification and regression problems. When many of the features are irrelevant, kernel methods such as the support vector machine and kernel ridge regression can sometimes perform poorly. We propose weighting the features wit…

2009-06-24abs ↗pdf ↗

A Unifying View of Explicit and Implicit Feature Maps of Graph Kernelscs.LG

Non-linear kernel methods can be approximated by fast linear ones using suitable explicit feature maps allowing their application to large scale problems. We investigate how convolution kernels for structured data are composed from base kernels and construct corresponding feature maps. On this basis we propose exact an…

2017-03-02abs ↗pdf ↗

Data Dependent Kernel Approximation using Pseudo Random Fourier Featurescs.LG

Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to the pairwise evaluations in kernel methods, the complexity of kernel computation grows as the data size increases; thus the applicability of kernel methods is limited for large scale datasets. Random Fourier Features (R…

2017-11-27abs ↗pdf ↗

Support Feature Machinescs.LG

SFM uses linear models in extended feature spaces to avoid kernel issues.

problem Difficulty in interpreting SVM solutions and limitations of single kernel types.

method Support Feature Machines (SFM) use linear models in extended feature spaces.

result SFM provides at least as good results as kernel-based SVMs without interpretation, scaling, and convergence issues.

Support Feature Machines SVM Kernel methods Feature space Linear models

2019-01-28abs ↗pdf ↗

Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learningcs.LG

A new kernel, Isolation Kernel, simplifies large scale online kernel learning without sacrificing accuracy.

problem Building efficient and scalable kernel-based models from large datasets with high accuracy.

method Introducing Isolation Kernel, which creates an exact, sparse, and finite-dimensional feature map of a kernel, allowing for efficient large scale online kernel learning without accuracy loss.

result Large scale online kernel learning can be achieved efficiently and accurately using Isolation Kernel.

kernel learning online learning large scale sparse feature map efficient computation

2019-07-02abs ↗pdf ↗

Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guaranteescs.LG

Paper improves understanding of random Fourier features for kernel ridge regression.

problem Understanding statistical properties of random Fourier features for kernel ridge regression.

method Spectral matrix approximation approach to analyze random Fourier features.

result Proves statistical guarantees for kernel ridge regression using random Fourier features.

random fourier features kernel ridge regression spectral approximation statistical guarantees matrix approximation

2018-04-26abs ↗pdf ↗

Interpretable Kernelsstat.ML

This paper makes kernels interpretable for wide feature matrices.

problem Loss of interpretability in kernel methods for wide feature matrices.

method Re-express kernel solution as a linear combination of original features with a special metric.

result Interpretable kernel solutions for wide feature matrices are possible.

interpretable AI kernel methods ridge regression linear combination wide feature matrices

2025-08-21abs ↗pdf ↗

Harmonizable mixture kernels with variational Fourier featuresstat.ML

New kernel HMK improves Gaussian process expressiveness and supports harmonizable covariances.

problem Improving the expressiveness of Gaussian processes with non-stationary kernels.

method Proposed harmonizable mixture kernel (HMK) and variational Fourier features.

result HMK interpolates between local patterns and offers robust kernel learning.

Gaussian processes non-stationary kernels harmonizable covariances variational Fourier features

2018-10-10abs ↗pdf ↗

Experimental kernel-based quantum machine learning in finite feature spacequant-ph

Quantum machine learning for 2D classification tasks using optimized feature maps.

problem Classifying data points in finite feature space with quantum machine learning.

method Optimized quantum feature maps and classical model training.

result Exponentially better scaling of deployed kernels in qubit number.

quantum machine learning kernel-based finite feature space two-dimensional classification exponential scaling

2019-06-10abs ↗pdf ↗

One-shot Distibuted Algorithm for PCA with RBF Kernelscs.LG

One-shot algorithm for feature-distributed kernel PCA reduces communication costs.

problem Efficiently perform kernel PCA in distributed computing environments.

method Inspired by dual relationship between sample-distributed and feature-distributed scenarios, proposes a one-shot algorithm for feature-distributed kernel PCA.

result The algorithm provides high-quality results with low communication costs, especially when eigenvalues decay fast.

kernel PCA distributed computing RBF kernels one-shot algorithm feature-distributed

2020-05-06abs ↗pdf ↗

No-Trick (Treat) Kernel Adaptive Filtering using Deterministic Featurescs.LG

No-trick kernel adaptive filtering uses deterministic features for scalability and robustness.

problem Scalability issues in kernel methods for large datasets.

method Deterministic feature-map construction using polynomial-exact solutions.

result Deterministic features outperform random Fourier features in performance and scalability.

kernel methods adaptive filtering scalability random Fourier features polynomial-exact solutions

2019-12-10abs ↗pdf ↗

Ridgeless Regression with Random Featurescs.LG

Kernel ridgeless regression with random features shows good generalization without explicit regularization.

problem Generalization of kernel ridgeless regression without explicit regularization.

method Investigation of ridgeless regression with random features and stochastic gradient descent, exploring the effect of random features error and spectral density optimization.

result Random features error exhibits the double-descent curve, leading to improved generalization.

ridgeless regression kernel ridgeless random features stochastic gradient descent double-descent

2022-05-01abs ↗pdf ↗

Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernelsstat.ML

New method reduces variance and bias in approximating indefinite kernels.

problem Approximating non-stationary indefinite kernels with low variance and bias.

method Generalized orthogonal random features (GORF)

result GORF achieves lower variance and approximation error compared to existing methods.

Random Fourier Features Kernel Approximation Indefinite Kernels Stationary Kernels Support Vector Machines

2021-04-13abs ↗pdf ↗

Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learningcs.LG

Improves MKL for multi-class classification with better feature selection and representation.

problem Real-world multi-class classification problems with non-linear separations.

method Large-margin multiple kernel learning (LMMK) with sparsity term for discriminative feature selection.

result Competitive classification accuracy and sparse non-zero kernel weights.

multiple kernel learning feature selection discriminative features sparse representation multi-class classification

2019-03-08abs ↗pdf ↗

Diagonal Over-parameterization in Reproducing Kernel Hilbert Spaces as an Adaptive Feature Model: Generalization and Adaptivitycs.LG

This paper improves neural network generalization by dynamically learning kernel parameters.

problem Improving neural network generalization and adaptability.

method Diagonal adaptive kernel model that learns kernel eigenvalues and output coefficients during training.

result The diagonal adaptive kernel model significantly improves generalization over fixed-kernel methods.

neural networks generalization adaptivity kernel methods feature learning

2025-01-15abs ↗pdf ↗

Streaming Kernel PCA with

\tilde{O}(\sqrt{n})

Random Featurescs.LG

New algorithm reduces kernel PCA to $O(\sqrt{n})$ features for streaming data.

problem Efficiently performing kernel PCA on large datasets.

method Random Fourier features and Oja's algorithm for streaming.

result Achieves

O(1/ε^2)

sample complexity with

O(\sqrt{n})

features.

Kernel PCA Random Fourier Features Streaming Algorithms Oja's Algorithm Sample Complexity

2018-08-02abs ↗pdf ↗

Adaptive kernel predictors from feature-learning infinite limits of neural networkscs.LG

Adaptive kernels from neural networks improve model performance.

problem Improving neural network performance through adaptive kernels.

method Deriving adaptive kernels from infinite-width neural networks using feature learning and gradient flow training.

result Adaptive kernels achieve lower test loss compared to traditional kernels.

neural networks kernel predictors infinite width feature learning gradient flow

2025-02-11abs ↗pdf ↗

Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoringstat.ML

Optimal scoring framework for kernel classification with feature selection.

problem Two-group classification problem.

method Optimal scoring framework, structured sparsity using weighted kernels, automated parameter selection.

result Superior classification performance compared to existing nonparametric classifiers.

kernel classification feature selection optimal scoring structured sparsity ridge parameter selection

2019-02-12abs ↗pdf ↗

RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Featuresstat.ML

RFFNet scales kernel methods to large datasets by learning kernel relevance.

problem Scaling kernel methods to large datasets while maintaining interpretability.

method Designs random Fourier features for ARD kernels and uses first-order stochastic optimization for learning kernel relevances.

result RFFNet achieves low prediction error and identifies relevant features, leading to more interpretable solutions.

kernel methods large-scale learning random Fourier features interpretable models ARD kernels

2022-11-11abs ↗pdf ↗

Hybrid Random Featurescs.LG

HRFs adaptively linearize kernels for accurate approximations.

problem Linearizing softmax and Gaussian kernels for machine learning applications.

method Generalizes Bochner's Theorem for kernels, uses random features for compositional kernels.

result Strong theoretical guarantees and unbiased approximation with smaller relative errors.

random features kernel approximation Bochner's Theorem softmax Gaussian

2021-10-08abs ↗pdf ↗

Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernelscs.LG

The paper develops a method to select features from multiple kernels for efficient risk minimization.

problem Identifying promising features leading to satisfactory out-of-sample performance in nonlinear kernel approximation.

method A greedy selection process using a correlation metric to choose features from multiple kernels.

result An out-of-sample error bound capturing trade-offs between approximation and spectral errors, showing poly-logarithmic scaling with data.

kernel approximation greedy selection feature maps multiple kernels out-of-sample error

2018-10-09abs ↗pdf ↗

Separation Results between Fixed-Kernel and Feature-Learning Probability Metricsstat.ML

New findings show fixed-kernel discriminators are weaker than feature-learning ones.

problem Comparing performance of fixed-kernel and feature-learning discriminators.

method Using function classes

\mathcal{F}_2

and

\mathcal{F}_1

, constructing pairs of distributions, and linking IPMs with sliced Wasserstein distances.

result Fixed-kernel IPM and SD cannot discriminate certain distributions that feature-learning IPM and SD can.

probability metrics discriminators fixed-kernel feature-learning sliced Wasserstein

2021-06-10abs ↗pdf ↗

Feature subset selection for kernel SVM classification via mixed-integer optimizationcs.LG

This paper uses MIO to select features for kernel SVM classification.

problem Feature selection for kernel SVM classification.

method Mixed-integer optimization (MIO) for feature subset selection.

result The MIO approach can often outperform linear-SVM-based methods in prediction performance.

feature selection kernel SVM mixed-integer optimization binary classification kernel-target alignment

2022-05-28abs ↗pdf ↗

Learning with Cross-Kernels and Ideal PCAcs.LG

We describe how cross-kernel matrices, that is, kernel matrices between the data and a custom chosen set of `feature spanning points' can be used for learning. The main potential of cross-kernels lies in the fact that (a) only one side of the matrix scales with the number of data points, and (b) cross-kernels, as oppos…

2014-06-10abs ↗pdf ↗

Quadrature-based features for kernel approximationcs.LG

We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. Based on an efficient numerical integration technique, we propose a unifying approach that…

2018-02-11abs ↗pdf ↗

High-Dimensional Feature Selection by Feature-Wise Kernelized Lassostat.ML

The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this pa…

2012-02-02abs ↗pdf ↗

Feature Understanding and Sparsity Enhancement via 2-Layered kernel machines (2L-FUSE)math.NA

2L-FUSE enhances feature sparsity through kernel learning.

problem Sparsity and feature selection in regression tasks.

method 2-Layered kernel machines for learning a shape matrix and feature direction identification.

result Minimal yet informative feature sets are identified without losing predictive performance.

sparsity enhancement kernel machines feature selection regression Mahalanobis distance

2025-09-09abs ↗pdf ↗

Scalable Kernel Learning via the Discriminant Informationcs.LG

New method optimizes kernel feature maps for better classification.

problem High computational and memory complexity of standard kernel methods.

method Discriminant Information criterion for optimizing kernel feature maps.

result Improved optimization and generalization performances over state-of-the-art methods.

kernel learning Discriminant Information classification optimization

2019-09-23abs ↗pdf ↗

Solving Interpretable Kernel Dimension Reductionstat.ML

Solves kernel dimension reduction while making features interpretable.

problem Making kernel dimension reduction methods interpretable.

method Projects onto a subspace before kernel feature mapping, using ISM for optimization.

result Extends ISM's theoretical guarantees to a family of kernels, enabling broader applicability.

kernel dimension reduction interpretable methods subspace projection iterative spectral method kernel optimization

2019-09-06abs ↗pdf ↗

Combining multiscale features for classification of hyperspectral images: a sequence based kernel approachcs.CV

Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a lon…

2016-06-15abs ↗pdf ↗

Lipschitz bounds for integral kernelsstat.ML

The paper studies Lipschitz bounds for integral kernels under differentiability assumptions.

problem Understanding the Lipschitz continuity of feature maps associated with integral kernels.

method Analyzes differentiability assumptions to derive explicit formulas for Lipschitz constants and conditions for non-Lipschitz continuity.

result Explicit formulas and conditions for Lipschitz continuity of feature maps associated with various kernels.

Lipschitz continuity integral kernels feature maps differentiability neural networks

2026-04-03abs ↗pdf ↗

The Mondrian Kernelstat.ML

We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selection procedure as the random features can be re-used efficiently for all kernel widths. The features are constructed by sampling trees via a…

2016-06-16abs ↗pdf ↗

RKHS-SHAP: Shapley Values for Kernel Methodsstat.ML

RKHS-SHAP uses Shapley values for kernel methods to provide feature attributions.

problem Feature attribution for kernel methods is often heuristic and not individualised.

method RKHS-SHAP uses Shapley values from coalition game theory to compute feature attributions for kernel machines efficiently.

result RKHS-SHAP can compute both Interventional and Observational Shapley values.

Shapley values kernel methods feature attribution kernel mean embeddings coalition game theory

2021-10-18abs ↗pdf ↗

Random Features for the Neural Tangent Kernelcs.LG

Efficiently applies NTK to large-scale datasets using random features.

problem Computational limitations of kernel methods for large-scale datasets.

method Proposes a sketching-based algorithm combining random features of arc-cosine kernels to construct an efficient feature map of the NTK.

result Achieves comparable error bounds to exact kernel methods but with significantly reduced feature dimensionality.

Neural Tangent Kernel Random Features Sketching Large-scale Datasets ReLU Networks

2021-04-03abs ↗pdf ↗

Local Random Feature Approximations of the Gaussian Kernelstat.ML

New method improves Gaussian kernel approximations for high-frequency data.

problem Limited scalability of kernel-based models to large data sets.

method Local random feature approximations using Maclaurin expansions and polynomial sketches.

result Significant improvement in kernel approximations and downstream performance for high-frequency data.

Gaussian kernel kernel approximation random features polynomial sketches high-frequency data

2022-04-12abs ↗pdf ↗

Random Fourier Features for Operator-Valued Kernelscs.LG

Devoted to multi-task learning and structured output learning, operator-valued kernels provide a flexible tool to build vector-valued functions in the context of Reproducing Kernel Hilbert Spaces. To scale up these methods, we extend the celebrated Random Fourier Feature methodology to get an approximation of operator-…

2016-05-09abs ↗pdf ↗

Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operatorsstat.ML

Random features improve neural operators' generalization properties.

problem Improving generalization of neural operators.

method Unified framework for spectral regularization techniques and operator-valued kernels.

result Established optimal learning rates and required number of neurons.

Random Features Operator-Valued Kernels Neural Operators Generalization Tikhonov Regularization

2026-03-01abs ↗pdf ↗

Graph Neural Networks with Composite Kernelscs.LG

Enhances graph neural networks by considering feature similarities in node aggregation.

problem Ignoring node feature similarities in traditional graph aggregation schemes.

method Interprets node aggregation as kernel weighting, proposing a framework that considers feature similarities.

result Proposed framework outperforms traditional GCNs in real-world applications.

Graph Neural Networks Kernel Weighting Feature Similarity Graph Attention Network Real-World Applications

2020-05-16abs ↗pdf ↗