Papers Sparse Learning
“Sparse Learning” 태그가 달린 논문 209편 · 필터 해제
Multi-task Sparse Learning with Beta Process Prior for Action Recognition
In this paper, we formulate human action recognition as a novel Multi-Task Sparse Learning(MTSL) framework which aims to construct a test sample with multiple features from as few bases as possible. Learning the sparse r…
Action RecognitionSparse LearningTemporal Action LocalizationA General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several spar…
Sparse LearningApproximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning
We consider the estimation of an i.i.d.\ vector $\xbf \in \R^n$ from measurements $\ybf \in \R^m$ obtained by a general cascade model consisting of a known linear transform followed by a probabilistic componentwise (poss…
compressed sensingparameter estimationSparse LearningAccelerated Training for Matrix-norm Regularization: A Boosting Approach
Sparse learning models typically combine a smooth loss with a nonsmooth penalty, such as trace norm. Although recent developments in sparse approximation have offered promising solution methods, current approaches either…
Multiview LearningSparse LearningNon-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels
We consider regularized risk minimization in a large dictionary of Reproducing kernel Hilbert Spaces (RKHSs) over which the target function has a sparse representation. This setting, commonly referred to as Sparse Mult…
Generalization BoundsSparse LearningEigenNet: A Bayesian hybrid of generative and conditional models for sparse learning
For many real-world applications, we often need to select correlated variables---such as genetic variations and imaging features associated with Alzheimer's disease---in a high dimensional space. The correlation between …
Sparse LearningVariable SelectionMultivariate Dyadic Regression Trees for Sparse Learning Problems
We propose a new nonparametric learning method based on multivariate dyadic regression trees (MDRTs). Unlike traditional dyadic decision trees (DDTs) or classification and regression trees (CARTs), MDRTs are constructed…
General ClassificationregressionSparse LearningVariable SelectionNonparametric Greedy Algorithms for the Sparse Learning Problem
This paper studies the forward greedy strategy in sparse nonparametric regression. For additive models, we propose an algorithm called additive forward regression; for general multivariate regression, we propose an algor…
Additive modelsregressionSparse LearningVariable SelectionAdaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models
Consider linear prediction models where the target function is a sparse linear combination of a set of basis functions. We are interested in the problem of identifying those basis functions with non-zero coefficients and…
Sparse Learning