Projects that are tagged with sparse representation.


Logo Identification of very short segments of identity by descent in NGS data 1.4.2

by hochreit - December 28, 2013, 17:22:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13823 views, 2578 downloads, 0 subscriptions

About: hapFabia is an R package for identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data.

Changes:

o citation update

o plot function improved


Logo Two dimensional relaxed representation 1.0

by openpr_nlpr - November 4, 2013, 05:48:12 CET [ Project Homepage BibTeX Download ] 3637 views, 822 downloads, 0 subscriptions

About: Q. Dong, Two-dimensional relaxed representation, Neurocomputing, 121:248-253, 2013, http://dx.doi.org/10.1016/j.neucom.2013.04.044

Changes:

Initial Announcement on mloss.org.


Logo Half quadratic based Iterative Minimization for Robust Sparse Representation 1.0

by openpr_nlpr - June 3, 2013, 09:57:11 CET [ Project Homepage BibTeX Download ] 3826 views, 1024 downloads, 0 subscriptions

About: Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explore their relation is still an open problem. In this paper, we develop a half-quadratic (HQ) framework to solve the robust sparse representation problem. By defining different kinds of half-quadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection. More specifically, by using the additive form of HQ, we propose an L1-regularized error correction method by iteratively recovering corrupted data from errors incurred by noises and outliers; by using the multiplicative form of HQ, we propose an L1-regularized error detection method by learning from uncorrupted data iteratively. We also show that the L1-regularization solved by soft-thresholding function has a dual relationship to Huber M-estimator, which theoretically guarantees the performance of robust sparse representation in terms of M-estimation. Experiments on robust face recognition under severe occlusion and corruption validate our framework and findings.

Changes:

Initial Announcement on mloss.org.


Logo Linear SVM with general regularization 1.0

by rflamary - October 5, 2012, 15:34:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7459 views, 2028 downloads, 0 subscriptions

About: This package is an implementation of a linear svm solver with a wide class of regularizations on the svm weight vector (l1, l2, mixed norm l1-lq, adaptive lasso). We provide solvers for the classical single task svm problem and for multi-task with joint feature selection or similarity promoting term.

Changes:

Initial Announcement on mloss.org.


Logo Sparse representation L1 minimization via half quadratic minimization 1.0

by openpr_nlpr - June 5, 2012, 11:33:58 CET [ Project Homepage BibTeX Download ] 4547 views, 1045 downloads, 0 subscriptions

About: Ran He, Wei-Shi Zheng,Tieniu Tan, and Zhenan Sun. Half-quadratic based Iterative Minimization for Robust Sparse Representation. Submitted to IEEE Trans. on Pattern Analysis and Machine Intelligence.

Changes:

Initial Announcement on mloss.org.