Robust integrated locally linear embedding

Authors:
Li-li Zhang;Ying Xie;Bin Luo;Chris Ding;Jin Tang
Affiliations:
Key Lab of Industrial Image Processing & Analysis of Anhui Province, Hefei, China, School of Computer Science and Technology, Anhui University, Hefei, China, CSE Department, University of Texa ...;Key Lab of Industrial Image Processing & Analysis of Anhui Province, Hefei, China, School of Computer Science and Technology, Anhui University, Hefei, China, CSE Department, University of Texa ...;Key Lab of Industrial Image Processing & Analysis of Anhui Province, Hefei, China, School of Computer Science and Technology, Anhui University, Hefei, China, CSE Department, University of Texa ...;Key Lab of Industrial Image Processing & Analysis of Anhui Province, Hefei, China, School of Computer Science and Technology, Anhui University, Hefei, China, CSE Department, University of Texa ...;Key Lab of Industrial Image Processing & Analysis of Anhui Province, Hefei, China, School of Computer Science and Technology, Anhui University, Hefei, China, CSE Department, University of Texa ...
Venue:
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Year:
2012

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Abstract

Many real life applications often bring much high-dimensional and noise-contaminated data from different sources. In this paper, we consider de-noising as well as dimensionality reduction by proposing a novel method named Robust Integrated Locally Linear Embedding. The method combines the two steps in LLE into a single framework and deals with de-noising by solving a l2,1-l2 mixed norm based optimization problem. We also derive an efficient algorithm to build the proposed model. Extensive experiments demonstrate that the proposed method is more suitable to exhibit relationship among data points, and has visible improvement in de-noising, embedding and clustering tasks.