Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples a…
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48 results for “sparse SVM”
This paper improves SVM prediction uncertainty quantification methods.
problem Lack of comprehensive UQ methods for SVM predictions.
method Developed SSVQR model for sparse PI estimation and feature selection algorithm.
result Proposed SSVQR model achieves sparse solutions and improves PI quality.
A sparse semismooth Newton based augmented Lagrangian method for large-scale support vector machinesmath.OC
Efficiently solves large-scale SVMs with sparse semismooth Newton method.
problem Numerical difficulties in solving large-scale SVMs.
method Sparse semismooth Newton based augmented Lagrangian method.
result Outperforms state-of-the-art solvers for large-scale SVMs.
Quantum LS-SVM simplifies matrix inversion for faster machine learning.
problem Speeding up machine learning algorithms for large datasets.
method Introduces a novel quantum algorithm using continuous variables to simplify matrix inversion in LS-SVM, and proposes a hybrid quantum-classical approach for sparse solutions.
result Quantum LS-SVM achieves exponential speed-up and can solve classically difficult tasks.
Quantum SVM uses fewer features for faster training.
problem Training high-dimensional SVMs efficiently.
method Quantum linear programming for sparse SVM training.
result Quantum sparse SVM can be trained in sublinear time.
New SVM model balances sparsity and robustness in noisy data.
problem Noise sensitivity and lack of sparsity in traditional SVM models.
method Combines elastic net loss with robust loss framework, integrates with SVM, uses half-quadratic algorithm.
result Proves sparsity and robustness, outperforms traditional SVMs in noisy environments.
Paper optimizes sparse feature selection for cancer detection using GSVP and SVM.
problem Sparse feature selection for cancer detection.
method Regularized GSVP with proximal gradient descent, feature selection via SVM.
result Near-perfect balanced accuracy with few selected features.
Improved Frank-Wolfe for sparse/low-rank problems.
problem Sparse/low-rank optimization problems.
method Primal-Dual Block Frank-Wolfe algorithm.
result Empirically outperforms state-of-the-art methods in classification tasks.
When applying the support vector machine (SVM) to high-dimensional classification problems, we often impose a sparse structure in the SVM to eliminate the influences of the irrelevant predictors. The lasso and other variable selection techniques have been successfully used in the SVM to perform automatic variable selec…
Support Vector Machines (SVMs) are powerful learners that have led to state-of-the-art results in various computer vision problems. SVMs suffer from various drawbacks in terms of selecting the right kernel, which depends on the image descriptors, as well as computational and memory efficiency. This paper introduces a n…
New research shows the maximum ℓ1-margin classifier doesn't adapt to sparse ground truths.
problem Understanding the limitations of the maximum ℓ1-margin classifier in high-dimensional settings.
method Analyzing convergence and prediction error rates of the maximum ℓ1-margin classifier.
result Proves tight upper and lower bounds for prediction error, showing benign overfitting.
The paper improves SVM for predicting hospital readmissions by clustering patients and personalizing recommendations.
problem Predicting and preventing hospital readmissions.
method Cluster-dependent SVM with personalized prescriptions.
result Personalized recommendations reduce hospital readmissions.
In this paper, we propose a semi-supervised dictionary learning method that uses both the information in labelled and unlabelled data and jointly trains a linear classifier embedded on the sparse codes. The manifold structure of the data in the sparse code space is preserved using the same approach as the Locally Linea…
This paper presents a unified framework to tackle estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The use of SVMs in estimation problems has been traditionally limited to its mere use as a black-box model. Noting such limitations in the literature, we take advantage of sever…
Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machinestat.ML
Paper tackles efficient SVM classification over decentralized networks.
problem Efficiently classifying high-dimensional data over decentralized networks.
method Convolution-based smoothing technique for nonsmooth hinge loss function, combined with an efficient ADMM algorithm.
result Provable linear convergence of the ADMM algorithm and near-optimal statistical convergence of the sparse estimator.
CoRE Kernelsstat.ML
The term "CoRE kernel" stands for correlation-resemblance kernel. In many applications (e.g., vision), the data are often high-dimensional, sparse, and non-binary. We propose two types of (nonlinear) CoRE kernels for non-binary sparse data and demonstrate the effectiveness of the new kernels through a classification ex…
We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood functio…
This work shows how disentangled and sparse representations improve multi-task learning.
problem Improving generalization in multi-task learning with disentangled and sparse representations.
method Proved a new identifiability result and proposed a practical approach using sparsity-promoting bi-level optimization.
result Maximally sparse base-predictors yield disentangled representations under certain conditions.
GPU optimization speeds up large-scale classification tasks.
problem Efficiently training large-scale classification models on GPUs.
method Judecious GPU-optimization principles applied to TRON algorithm.
result Significant speedups for logistic regression and SVM classification.
Support vector machines (SVMs) naturally embody sparseness due to their use of hinge loss functions. However, SVMs can not directly estimate conditional class probabilities. In this paper we propose and study a family of coherence functions, which are convex and differentiable, as surrogates of the hinge function. The …
Recently, there has been a renewed interest in the machine learning community for variants of a sparse greedy approximation procedure for concave optimization known as {the Frank-Wolfe (FW) method}. In particular, this procedure has been successfully applied to train large-scale instances of non-linear Support Vector M…
Proposed SMO algorithm for OC-SVM+ significantly outperforms non-sequential algorithms.
problem One-class SVM with privileged information
method Sequential Minimal Optimization (SMO) algorithm
result Finite-time convergence established
Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper we bring them into a unified view and show that they have a much stronger relation than what is commonly thought. We analyze SVMs from a metr…
Unified Pin-SVM improves accuracy over existing Pin-SVM model.
problem Difficulty in Pin-SVM model for .
method Unified Pin-SVM model that solves a QPP for .
result Significant improvement in accuracy over existing Pin-SVM model.
Localized SVMs maintain SVM's consistency properties for large datasets.
problem Inefficient computational requirements of global SVMs for large data sets.
method Localized SVMs apply different hyperparameters to different regions of the input space.
result Localized SVMs inherit - and risk consistency from global SVMs.
Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and b…
Robust support vector model based on bounded asymmetric elastic net loss for binary classificationstat.ML
BAEN-SVM improves SVM robustness to noisy data.
problem Noise and geometric irrationalities in SVM.
method Bounded asymmetric elastic net loss combined with SVM.
result BAEN-SVM is robust to noise and geometrically well-defined.
Sparse optimization refers to an optimization problem involving the zero-norm in objective or constraints. In this paper, nonconvex approximation approaches for sparse optimization have been studied with a unifying point of view in DC (Difference of Convex functions) programming framework. Considering a common DC appro…
New SVM feature selection methods improve wafer testing accuracy.
problem Need for improved SVM feature selection methods.
method Entropy measurement and K-medoid clustering for feature selection.
result One-class SVM's enhance wafer testing accuracy.
Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this paper, thus…
SMM preserves matrix data structure for SVM classification.
problem Preserving spatial correlations in matrix data for SVM.
method SMM uses spectral elastic net combining nuclear and Frobenius norms.
result SMM improves SVM performance on matrix data.
A quantum-inspired classical algorithm speeds up LS-SVM classification.
problem Big data challenge in SVM classification.
method Improved indirect sampling technique for LS-SVM.
result Algorithm achieves logarithmic runtime for low rank data matrices.
Pixel-wise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. By representing a test pixel as a linear combination of a small subset of labeled pixels, a sparse representation classifier (SRC) gives rather plausible results …
As enjoying the closed form solution, least squares support vector machine (LSSVM) has been widely used for classification and regression problems having the comparable performance with other types of SVMs. However, LSSVM has two drawbacks: sensitive to outliers and lacking sparseness. Robust LSSVM (R-LSSVM) overcomes …
Introduces Soft-SVM for binary classification bridging logistic and SVM.
problem Data separability issues in binary classification.
method Soft-SVM regression using convex relaxation of hinge loss with softness and class-separation parameters.
result Soft-SVM performs well in classification and prediction errors.
In this paper, we consider asymptotic properties of the support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. We show that the hard-margin linear SVM holds a consistency property in which misclassification rates tend to zero as the dimension goes to infinity under certain severe conditions. …
Paper proposes an ensemble SVM method for efficient VAD.
problem Efficient and accurate VAD for speech processing.
method Supervised learning with ensemble SVM on large datasets.
result Ensemble SVM outperforms stand-alone SVM in VAD accuracy.
Proposes SVM-based Deep Stacking Network for improved deep learning.
problem Improving deep learning performance and interpretability.
method Uses stacked SVM classifiers within a DSN architecture and a BP-like layer tuning scheme.
result Demonstrates superior performance compared to benchmark models on image and text data.
SpicyMKLstat.ML
We propose a new optimization algorithm for Multiple Kernel Learning (MKL) called SpicyMKL, which is applicable to general convex loss functions and general types of regularization. The proposed SpicyMKL iteratively solves smooth minimization problems. Thus, there is no need of solving SVM, LP, or QP internally. SpicyM…
Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information avai…
MKL--SVMstat.ML
Paper introduces MKL--SVM for SVM with loss.
problem Optimization of SVM with loss function.
method MKL framework combined with ADMM algorithm for solving the optimization problem.
result Performance of MKL--SVM comparable to SimpleMKL.
Weighted SVM (or fuzzy SVM) is the most widely used SVM variant owning its effectiveness to the use of instance weights. Proper selection of the instance weights can lead to increased generalization performance. In this work, we extend the span error bound theory to weighted SVM and we introduce effective hyperparamete…
Paper tackles efficient training of linear models on manycore processors.
problem Training generalized linear models on manycore processors efficiently.
method Proposes Heterogeneous Tasks on Homogeneous Cores (HTHC) approach for parallelism.
result Improves state-of-the-art Lasso training by an order of magnitude.
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, which require solving a …
We describe a novel binary classification technique called Banded SVM (B-SVM). In the standard C-SVM formulation of Cortes et al. (1995), the decision rule is encouraged to lie in the interval [1, \infty]. The new B-SVM objective function contains a penalty term that encourages the decision rule to lie in a user specif…
SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.
problem Classifying hyperspectral images with limited labeled data.
method Shape-adaptive Reconstruction (SaR) for pixel preprocessing, SVM for probability estimation, and Smoothed Total Variation (STV) for denoising.
result SaR-SVM-STV outperforms SVM-STV with fewer labeled data.
SVM used for estimating treatment effects without confounding.
problem Estimating average treatment effects in the presence of confounding variables.
method Adapts SVM classifier as a kernel-based weighting procedure to balance covariates and estimate causal effects.
result SVM provides a continuous relaxation of the quadratic integer program for balancing covariates and maximizing effective sample size.
We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We also propose an analytic span bound for model selection with almost 2-4x faster computation times than standard resampling techniques. We empirically demonstrate the efficacy of the proposed…