A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries

被引：17

作者：

Agarwal, Alekh ^{[1
]}

Anandkumar, Animashree ^{[2
]}

Netrapalli, Praneeth ^{[3
]}

机构：

[1] Microsoft Res, New York, NY 10011 USA

[2] Univ Calif Irvine, Ctr Pervas Commun & Comp, Elect Engn & Comp Sci Dept, Irvine, CA 92697 USA

[3] Microsoft Res, Bengaluru 10011, India

来源：

IEEE TRANSACTIONS ON INFORMATION THEORY | 2017年 / 63卷 / 01期

关键词：

Dictionary learning; sparse coding; overcomplete dictionaries; incoherence; lasso; SAMPLE COMPLEXITY;

D O I：

10.1109/TIT.2016.2614684

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We consider the problem of learning overcomplete dictionaries in the context of sparse coding, where each sample selects a sparse subset of dictionary elements. Our main result is a strategy to approximately recover the unknown dictionary using an efficient algorithm. Our algorithm is a clustering-style procedure, where each cluster is used to estimate a dictionary element. The resulting solution can often be further cleaned up to obtain a high accuracy estimate, and we provide one simple scenario where l(1)-regularized regression can be used for such a second stage.

引用

页码：575 / 592

页数：18

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