Title
Spectral Clustering of High-Dimensional Data via k-Nearest Neighbor Based Sparse Representation Coefficients
Abstract
Recently, subspace clustering has achieved promising clustering quality by performing spectral clustering over an affinity graph. It is a key to construct a robust affinity matrix in graph-oriented subspace clustering. Sparse representation can represent each object as a sparse linear combination of other objects and has been used to cluster high-dimensional data. However, all the coefficients are trusted blindly to construct the affinity matrix which may suffer from noise and decrease the clustering performance. We propose to construct the affinity matrix via k-nearest neighbor (KNN) based sparse representation coefficient vectors for clustering high-dimensional data. For each data object, the sparse representation coefficient vector is computed by sparse representation theory and KNN algorithm is used to find the k nearest neighbors. Instead of using all the coefficients to construct the affinity matrix directly, we update each coefficient vector by remaining the k coefficients of the k neighbors unchanged and set the other coefficients to zero. Experiments on six gene expression profiling (GEP) datasets prove that the proposed algorithm can construct better affinity matrices and result in higher performance for clustering high-dimensional data.
Year
DOI
Venue
2015
10.1007/978-3-319-22053-6_40
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, ICIC 2015, PT III
Keywords
DocType
Volume
Spectral clustering,Affinity matrix,k-nearest neighbor,Sparse representation,Cosine similarity,Gene expression profiling
Conference
9227
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
7
3
Name
Order
Citations
PageRank
Fang Chen15310.84
Shulin Wang2277.13
Jianwen Fang314814.82