Clustering by learning the non-negative half-space
Hu K.; Tian J.; Tang Y.Y.
Source PublicationInternational Conference on Wavelet Analysis and Pattern Recognition
AbstractThis paper proposes a novel clustering algorithm which is called Non-negative Half-space Clustering (NHC), by revealing the nonnegative half-space structure of samples. The half-space is the union of some nearly independent half-spaces, and each class of samples is dominated by this half-space. Since the subspace independent assumption is not imposed on the samples, NHC is robust for the increasing of number of classes compared with other subspace clustering methods such as Sparse Space Clustering. After obtaining a half-space structure, the adjacency graph is almost block-wise, and can be well grouped by some cutting techniques. In the experiment section, we implement NHC and other competitive algorithms on two database CBCL and Reuters-21578. The result shows that NHC performs better on the two database, and more robust than SSC.
KeywordClustering Half-space Non-negative representation
URLView the original
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Document TypeConference paper
CollectionUniversity of Macau
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Hu K.,Tian J.,Tang Y.Y.. Clustering by learning the non-negative half-space[C],2018:36-41.
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