Spectral clustering of high-dimensional data exploiting sparse representation vectors

Wu, Sen; Feng, Xiaodong; Zhou, Wenjun

doi:10.1016/j.neucom.2013.12.027

Cited by 47 publications

(26 citation statements)

References 51 publications

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“…Common alternatives to subspace clustering include approximate expectation maximization (EM) [23], spectral clustering [89], shared-neighbor methods [46,91,93] and relevant set correlation [41,88], and clustering ensembles [31,32].…”

Section: Clustering Techniques For High-dimensional Datamentioning

confidence: 99%

Clustering Evaluation in High-Dimensional Data

Tomašev

Radovanović

2016

Unsupervised Learning Algorithms

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Clustering evaluation plays an important role in unsupervised learning systems, as it is often necessary to automatically quantify the quality of generated cluster configurations. This is especially useful for comparing the performance of different clustering algorithms as well as determining the optimal number of clusters in clustering algorithms that do not estimate it internally. Many clustering quality indexes have been proposed over the years and different indexes are used in different contexts. There is no unifying protocol for clustering evaluation, so it is often unclear which quality index to use in which case. In this chapter, we review the existing clustering quality measures and evaluate them in the challenging context of high-dimensional data clustering. High-dimensional data is sparse and distances tend to concentrate, possibly affecting the applicability of various clustering quality indexes. We analyze the stability and discriminative power of a set of standard clustering quality measures with increasing data dimensionality. Our evaluation shows that the curse of dimensionality affects different clustering quality indexes in different ways and that some are to be preferred when determining clustering quality in many dimensions.

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Section: Clustering Techniques For High-dimensional Datamentioning

confidence: 99%

Clustering Evaluation in High-Dimensional Data

Tomašev

Radovanović

2016

Unsupervised Learning Algorithms

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“…This algorithm is mainly used to compare the performance of feature selection. Test data sets are USPS, UMIST [19], COIL20, and Isolet [5], the data sets are described as shown in Table 1. In this paper, two evaluation metrics are used to evaluate the clustering performance of the various algorithms, they are the accuracy rate (AC),and the normalized mutual information value (NMI) [19].…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Experiments show the superiority of the proposed algorithm. Sparse representation has been demonstrated to be effective in dealing with high-dimensional data, Wu et al [5] proposed a spectral algorithm with two weight matrix based on sparse representation vector. Experiments of real high dimensional data show that the performance of the algorithm is higher than the existing spectral clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Novel Spectral Clustering Based on Nonlinear Low Dimensional Embedding Feature Selection

Zhang¹,

Zhou²

2016

IJGDC

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“…For example, Wright et al [5] used individual sparse coefficient directly to build the affinity matrix for spectral clustering. However, Wu et al [6] proved that exploiting complete sparse representation vectors can reflect more truthful similarity among data objects, since more contextual information is taken into consideration. They assumed that the sparse representation vectors corresponding to two similar objects should be similar, since they can be reconstructed in a similar fashion using other data objects.…”

mentioning

confidence: 99%

“…To improve the performance of sparse representation based spectral clustering (SRSC) [6], we propose a novel clustering algorithm for high-dimensional data, which constructs affinity matrix via KNN based sparse representation coefficient vectors. We name our proposed algorithm as KNNSRSC.…”

mentioning

confidence: 99%

Spectral Clustering of High-Dimensional Data via k-Nearest Neighbor Based Sparse Representation Coefficients

Chen

Fang²

2015

Lecture Notes in Computer Science

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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.

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Spectral clustering of high-dimensional data exploiting sparse representation vectors

Cited by 47 publications

References 51 publications

Clustering Evaluation in High-Dimensional Data

Clustering Evaluation in High-Dimensional Data

A Novel Spectral Clustering Based on Nonlinear Low Dimensional Embedding Feature Selection

Spectral Clustering of High-Dimensional Data via k-Nearest Neighbor Based Sparse Representation Coefficients

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