Spare Projections with Pairwise Constraints

In this paper, we propose a new semi-supervised DR method called sparse projections with pairwise constraints (SPPC). Unlike many existing techniques such as locality preserving projection (LPP) and semi-supervised DR (SSDR), where local or global information is preserved during the DR procedure, SP...

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Bibliographic Details
Published inProcedia engineering Vol. 29; pp. 1028 - 1033
Main Authors Chen, Xiaodong, Yu, Jiangfeng
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 2012
Subjects
Dimensionality reduction
global and local structures
Pairwise constraints
Sparse projections
Sparse projections
Dimensionality reduction
Pairwise constraints
global and local structures
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Summary:In this paper, we propose a new semi-supervised DR method called sparse projections with pairwise constraints (SPPC). Unlike many existing techniques such as locality preserving projection (LPP) and semi-supervised DR (SSDR), where local or global information is preserved during the DR procedure, SPPC constructs a graph embedding model, which encodes the global and local geometrical structures in the data as well as the pairwise constraints. After obtaining the embedding results, sparse projections can be acquired by minimizing a L1 regularization-related objective function. Experiments on real-world data sets show that SPPC is superior to many established dimensionality reduction methods.
ISSN:1877-7058
1877-7058
DOI:10.1016/j.proeng.2012.01.084