Dictionary learning based on laplacian score in sparse coding

Jin Xu, Hong Man

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

Sparse coding, which produces a vector representation based on sparse linear combination of dictionary atoms, has been widely applied in signal processing, data mining and neuroscience. Constructing a proper dictionary for sparse coding is a common challenging problem. In this paper, we treat dictionary learning as an unsupervised learning process, and propose a Laplacian score dictionary (LSD). This new learning method uses local geometry information to select atoms for the dictionary. Comparisons with alternative clustering based dictionary learning methods are conducted. We also compare LSD with full-training-data-dictionary and others classic methods in the experiments. The classification performances on binary-class datasets and multi-class datasets from UCI repository demonstrate the effectiveness and efficiency of our method.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 7th International Conference, MLDM 2011, Proceedings
Pages253-264
Number of pages12
DOIs
StatePublished - 2011
Event7th International Conference on Machine Learning and Data Mining, MLDM 2011 - New York, NY, United States
Duration: 30 Aug 20113 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6871 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Machine Learning and Data Mining, MLDM 2011
Country/TerritoryUnited States
CityNew York, NY
Period30/08/113/09/11

Keywords

  • Clustering
  • Dictionary learning
  • Laplacian Score
  • Sparse coding
  • Unsupervised learning

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