Papers Sparse Learning
“Sparse Learning” 태그가 달린 논문 209편 · 필터 해제
Similarity Preserving Unsupervised Feature Selection based on Sparse Learning
Various feature selection methods have been recently proposed on different applications to reduce the computational burden of machine learning algorithms as well as the complexity of learned models. Preserving sample sim…
feature selectionSparse LearningKNN Classification with One-step Computation
KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space…
ClassificationGeneral ClassificationSparse LearningThunder: a Fast Coordinate Selection Solver for Sparse Learning
L1 regularization has been broadly employed to pursue model sparsity. Despite the non-smoothness, people have developed efficient algorithms by leveraging the sparsity and convexity of the problems. In this paper, we pro…
Sparse LearningAccelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties
Nesterov's accelerated gradient (AG) is a popular technique to optimize objective functions comprising two components: a convex loss and a penalty function. While AG methods perform well for convex penalties, such as the…
DenoisingImage DenoisingSparse LearningVariable SelectionEffective Proximal Methods for Non-convex Non-smooth Regularized Learning
Sparse learning is a very important tool for mining useful information and patterns from high dimensional data. Non-convex non-smooth regularized learning problems play essential roles in sparse learning, and have drawn …
Sparse LearningBlock-wise Minimization-Majorization algorithm for Huber's criterion: sparse learning and applications
Huber's criterion can be used for robust joint estimation of regression and scale parameters in the linear model. Huber's (Huber, 1981) motivation for introducing the criterion stemmed from non-convexity of the joint max…
DenoisingImage DenoisingSparse LearningAdaptive support driven Bayesian reweighted algorithm for sparse signal recovery
Sparse learning has been widely studied to capture critical information from enormous data sources in the filed of system identification. Often, it is essential to understand internal working mechanisms of unknown system…
feature selectionSparse LearningEvent Enhanced High-Quality Image Recovery
With extremely high temporal resolution, event cameras have a large potential for robotics and computer vision. However, their asynchronous imaging mechanism often aggravates the measurement sensitivity to noises and bri…
DenoisingSparse LearningSuper-ResolutionVocal Bursts Intensity PredictionFast OSCAR and OWL Regression via Safe Screening Rules
Ordered Weighted $L_{1}$ (OWL) regularized regression is a new regression analysis for high-dimensional sparse learning. Proximal gradient methods are used as standard approaches to solve OWL regression. However, it is s…
regressionSparse LearningBayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications
In this work, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trai…
Sparse LearningPicasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear regression, sparse logistic regression, spa…
regressionSparse LearningSWAG: A Wrapper Method for Sparse Learning
The majority of machine learning methods and algorithms give high priority to prediction performance which may not always correspond to the priority of the users. In many cases, practitioners and researchers in different…
AttributeDiagnosticDiversitySparse LearningSparse learning with CART
Decision trees with binary splits are popularly constructed using Classification and Regression Trees (CART) methodology. For regression models, this approach recursively divides the data into two near-homogenous daughte…
regressionSparse LearningA Sparse Learning Approach to the Design of Radar Tunable Architectures with Enhanced Selectivity Properties
This paper considers the design of tunable decision schemes capable of rejecting with high probability mismatched signals embedded in Gaussian interference with unknown covariance matrix. To this end, a sparse recovery t…
Sparse LearningSparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization
We consider the least squares regression problem, penalized with a combination of the $\ell_{0}$ and squared $\ell_{2}$ penalty functions (a.k.a. $\ell_0 \ell_2$ regularization). Recent work shows that the resulting esti…
regressionSparse LearningClassification of Chinese Handwritten Numbers with Labeled Projective Dictionary Pair Learning
Dictionary learning is a cornerstone of image classification. We set out to address a longstanding challenge in using dictionary learning for classification; that is to simultaneously maximise the discriminability and sp…
ClassificationDictionary LearningGeneral Classificationimage-classification+2Adaptive Neural Connections for Sparsity Learning
Sparsity learning aims to decrease the computational and memory costs of large deep neural networks (DNNs) via pruning neural connections while simultaneously retaining high accuracy. A large body of work has developed s…
Model CompressionNetwork PruningSparse LearningLearning High Order Feature Interactions with Fine Control Kernels
We provide a methodology for learning sparse statistical models that use as features all possible multiplicative interactions among an underlying atomic set of features. While the resulting optimization problems are expo…
Sparse LearningVocal Bursts Intensity PredictionOn Newton Screening
Screening and working set techniques are important approaches to reducing the size of an optimization problem. They have been widely used in accelerating first-order methods for solving large-scale sparse learning proble…
Sparse LearningSparse Weight Activation Training
Neural network training is computationally and memory intensive. Sparse training can reduce the burden on emerging hardware platforms designed to accelerate sparse computations, but it can affect network convergence. In …
Image ClassificationNetwork PruningSparse Learning