Paper Info
Title
Pairwise Sparsity Preserving Embedding for Unsupervised Subspace Learning and Classification
Abstract
Two novel unsupervised dimensionality reduction techniques, termed sparse distance preserving embedding (SDPE) and sparse proximity preserving embedding (SPPE), are proposed for feature extraction and classification. SDPE and SPPE perform in the clean data space recovered by sparse representation and enhanced Euclidean distances over noise removed data are employed to measure pairwise similarities of points. In extracting informative features, SDPE and SPPE aim at preserving pairwise similarities between data points in addition to preserving the sparse characteristics. This paper calculates the sparsest representation of all vectors jointly by a convex optimization. The sparsest codes enable certain local information of data to be preserved, and can endow SDPE and SPPE a natural discriminating power, adaptive neighborhood and robust characteristic against noise and errors in delivering low-dimensional embeddings. We also mathematically show SDPE and SPPE can be effectively extended for discriminant learning in a supervised manner. The validity of SDPE and SPPE is examined by extensive simulations. Comparison with other related state-of-the-art unsupervised algorithms show that promising results are delivered by our techniques.
Year
DOI
Venue
2013
10.1109/TIP.2013.2277780
IEEE Transactions on Image Processing
Keywords
Field
DocType
classification,feature extraction,sparse representation,unsupervised subspace learning,convex programming,unsupervised learning,data compression
Data point,Pairwise comparison,Embedding,Dimensionality reduction,Subspace topology,Pattern recognition,Sparse approximation,Feature extraction,Unsupervised learning,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
22
12
1941-0042
Citations 
PageRank 
References 
23
0.66
27
Authors
3
Name
Order
Citations
PageRank
Zhao Zhang193865.99
Shuicheng Yan276725.71
Mingbo Zhao363136.16