Papers Sparse Learning
“Sparse Learning” 태그가 달린 논문 209편 · 필터 해제
Sparse learning of maximum likelihood model for optimization of complex loss function
Traditional machine learning methods usually minimize a simple loss function to learn a predictive model, and then use a complex performance measure to measure the prediction performance. However, minimizing a simple los…
image-classificationImage ClassificationSparse LearningSparse Learning for Large-scale and High-dimensional Data: A Randomized Convex-concave Optimization Approach
In this paper, we develop a randomized algorithm and theory for learning a sparse model from large-scale and high-dimensional data, which is usually formulated as an empirical risk minimization problem with a sparsity-in…
Sparse LearningParametric Maxflows for Structured Sparse Learning with Convex Relaxations of Submodular Functions
The proximal problem for structured penalties obtained via convex relaxations of submodular functions is known to be equivalent to minimizing separable convex functions over the corresponding submodular polyhedra. In thi…
Sparse LearningBest Subset Selection via a Modern Optimization Lens
In the last twenty-five years (1990-2014), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 200 billion factor speedup in solving Mixed Integer Optimization…
regressionSparse LearningSafe Feature Pruning for Sparse High-Order Interaction Models
Taking into account high-order interactions among covariates is valuable in many practical regression problems. This is, however, computationally challenging task because the number of high-order interaction features to …
Sparse LearningVocal Bursts Intensity PredictionDirect l_(2,p)-Norm Learning for Feature Selection
In this paper, we propose a novel sparse learning based feature selection method that directly optimizes a large margin linear classification model sparsity with l_(2,p)-norm (0 < p < 1)subject to data-fitting constraint…
feature selectionGeneral ClassificationSparse LearningA Nonconvex Approach for Structured Sparse Learning
Sparse learning is an important topic in many areas such as machine learning, statistical estimation, signal processing, etc. Recently, there emerges a growing interest on structured sparse learning. In this paper we foc…
Sparse LearningStatistical Limits of Convex Relaxations
Many high dimensional sparse learning problems are formulated as nonconvex optimization. A popular approach to solve these nonconvex optimization problems is through convex relaxations such as linear and semidefinite pro…
Sparse LearningStochastic Block ModelPathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory
The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorit…
parameter estimationSparse LearningAccelerated Mini-batch Randomized Block Coordinate Descent Method
We consider regularized empirical risk minimization problems. In particular, we minimize the sum of a smooth empirical risk function and a nonsmooth regularization function. When the regularization function is block sepa…
Sparse LearningStochastic OptimizationPartial Occlusion Handling for Visual Tracking via Robust Part Matching
Part-based visual tracking is advantageous due to its robustness against partial occlusion. However, how to effectively exploit the confidence scores of individual parts to construct a robust tracker is still a challengi…
Occlusion HandlingSparse LearningVisual TrackingA Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not centrally located but…
Sparse LearningBeyond L2-Loss Functions for Learning Sparse Models
Incorporating sparsity priors in learning tasks can give rise to simple, and interpretable models for complex high dimensional data. Sparse models have found widespread use in structure discovery, recovering data from co…
ClusteringRetrievalSparse LearningTAGSparse Learning over Infinite Subgraph Features
We present a supervised-learning algorithm from graph data (a set of graphs) for arbitrary twice-differentiable loss functions and sparse linear models over all possible subgraph features. To date, it has been shown that…
AllSparse LearningCan Image-Level Labels Replace Pixel-Level Labels for Image Parsing
This paper presents a weakly supervised sparse learning approach to the problem of noisily tagged image parsing, or segmenting all the objects within a noisily tagged image and identifying their categories (i.e. tags). D…
AllSparse LearningTensor Representation and Manifold Learning Methods for Remote Sensing Images
One of the main purposes of earth observation is to extract interested information and knowledge from remote sensing (RS) images with high efficiency and accuracy. However, with the development of RS technologies, RS sys…
Earth ObservationSparse LearningTransfer LearningSafe and Efficient Screening For Sparse Support Vector Machine
Screening is an effective technique for speeding up the training process of a sparse learning model by removing the features that are guaranteed to be inactive the process. In this paper, we present a efficient screening…
Sparse LearningCompound Poisson Processes, Latent Shrinkage Priors and Bayesian Nonconvex Penalization
In this paper we discuss Bayesian nonconvex penalization for sparse learning problems. We explore a nonparametric formulation for latent shrinkage parameters using subordinators which are one-dimensional L\'{e}vy process…
regressionSparse LearningSafe Screening With Variational Inequalities and Its Application to LASSO
Sparse learning techniques have been routinely used for feature selection as the resulting model usually has a small number of non-zero entries. Safe screening, which eliminates the features that are guaranteed to have z…
Computational Efficiencyfeature selectionSparse LearningEfficient Mixed-Norm Regularization: Algorithms and Safe Screening Methods
Sparse learning has recently received increasing attention in many areas including machine learning, statistics, and applied mathematics. The mixed-norm regularization based on the l1q norm with q>1 is attractive in many…
Sparse Learning