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.
Neural network learns kernel functions for survival analysis and prediction intervals.
The kernel matrix used in kernel methods encodes all the information required for solving complex nonlinear problems defined on data representations in the input space using simple, but implicitly defined, solutions. Spectral analysis on the kernel matrix defines an explicit nonlinear mapping of the input data represen…
Kernel discriminant analysis uses nonlinear embeddings to improve classification.
Paper introduces RKHM and KME for richer data analysis.
We present a new method which generalizes subspace learning based on eigenvalue and generalized eigenvalue problems. This method, Roweis Discriminant Analysis (RDA), is named after Sam Roweis to whom the field of subspace learning owes significantly. RDA is a family of infinite number of algorithms where Principal Comp…
Complex analysis techniques link Gaussian RBF kernels to quantum mechanics.
We consider the problem of learning regression functions from pairwise data when there exists prior knowledge that the relation to be learned is symmetric or anti-symmetric. Such prior knowledge is commonly enforced by symmetrizing or anti-symmetrizing pairwise kernel functions. Through spectral analysis, we show that …
Sentiment analysis consists of evaluating opinions or statements from the analysis of text. Among the methods used to estimate the degree in which a text expresses a given sentiment, are those based on Gaussian Processes. However, traditional Gaussian Processes methods use a predefined kernel with hyperparameters that …
Paper introduces RKHM for more explicit variable structures analysis.
New kernel method for shape classification on Kendall shape space.
Topological data analysis and its main method, persistent homology, provide a toolkit for computing topological information of high-dimensional and noisy data sets. Kernels for one-parameter persistent homology have been established to connect persistent homology with machine learning techniques. We contribute a kernel…
Survival kernets scale deep kernel survival analysis to large datasets with interpretability and theoretical guarantees.
Signature kernel handles sequential data with theoretical and practical advantages.
New theoretical tools simplify kernel-based tests analysis.
RKUM is an R package for robust kernel-based unsupervised methods.
Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well…
Proposes KMvDA for object recognition from multi-view data.
Generalizes neural tangent kernel analysis for two-layer networks with noise and regularization.
Survey on manifold ends with new heat kernel estimates.
The study extends kernel universality to Riemannian symmetric spaces.
Kernel alignment measures the degree of similarity between two kernels. In this paper, inspired from kernel alignment, we propose a new Linear Discriminant Analysis (LDA) formulation, kernel alignment LDA (kaLDA). We first define two kernels, data kernel and class indicator kernel. The problem is to find a subspace to …
Study on reducing dimensionality in high-dimensional regression with kernel methods and stability analysis.
This paper presents a stochastic behavior analysis of a kernel-based stochastic restricted-gradient descent method. The restricted gradient gives a steepest ascent direction within the so-called dictionary subspace. The analysis provides the transient and steady state performance in the mean squared error criterion. It…
Two-layer neural networks learn efficiently using kernel methods in mean-field analysis.
Criterion extends identifiability for continuous mixtures of kernels.
Survey of kernels, RKHS, and their applications in machine learning.
Study spectral properties of graph Laplacian for manifold data.
Imaging genetic research has essentially focused on discovering unique and co-association effects, but typically ignoring to identify outliers or atypical objects in genetic as well as non-genetics variables. Identifying significant outliers is an essential and challenging issue for imaging genetics and multiple source…
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data. Kernel and Multiple Kernel CCA are popular methods for finding nonlinear correlations between high-dimensional datasets. Data was gathered from …
In genome-wide interaction studies, to detect gene-gene interactions, most methods are divided into two folds: single nucleotide polymorphisms (SNP) based and gene-based methods. Basically, the methods based on the gene are more effective than the methods based on a single SNP. Recent years, while the kernel canonical …
Enhanced kernel ridgeless regression improves performance with LAB RBF kernels.
Paper develops a dual formulation for PCA in Hilbert spaces.
A mean function in reproducing kernel Hilbert space, or a kernel mean, is an important part of many applications ranging from kernel principal component analysis to Hilbert-space embedding of distributions. Given finite samples, an empirical average is the standard estimate for the true kernel mean. We show that this e…
Bayesian model merges multi-view latent models and kernel methods.
To the best of our knowledge, there are no general well-founded robust methods for statistical unsupervised learning. Most of the unsupervised methods explicitly or implicitly depend on the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). They are sensitive to contaminated data, …
New PCA method detects faults using occupation kernels.
Kernel testing compares cell states in single-cell data.
PGF kernels analyze spherical data using generalized RBF kernels.
Study of regularized least squares in RKKS with indefinite kernels.
NKI integrates obfuscated datasets using nonlinear kernels for improved data collaboration.
Canonical correlation analysis (CCA) is a multivariate statistical technique for finding the linear relationship between two sets of variables. The kernel generalization of CCA named kernel CCA has been proposed to find nonlinear relations between datasets. Despite their wide usage, they have one common limitation that…
Unified analysis of kernel-based and locally adaptive bandit optimization methods.
The ratio of two probability densities can be used for solving various machine learning tasks such as covariate shift adaptation (importance sampling), outlier detection (likelihood-ratio test), and feature selection (mutual information). Recently, several methods of directly estimating the density ratio have been deve…
This paper examines the problem of learning with a finite and possibly large set of p base kernels. It presents a theoretical and empirical analysis of an approach addressing this problem based on ensembles of kernel predictors. This includes novel theoretical guarantees based on the Rademacher complexity of the corres…
Kernelmethods library simplifies kernel-based ML in Python.
Kernelized cumulants improve statistical analysis in high-dimensional spaces.
Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit non-linear representation of the data. A dominant concern is the memory requirement since memory scales as the square of the number of data points. We provide a new analysis of a class of approximate kernel methods…
Paper extends RPD for better handling multiple modalities and non-convexity.