Divide-and-conquer based Large-Scale Spectral Clustering (2021-04-30T00:00:00.000000Z)

TL;DR

In the proposed method, a divide-and-conquer based landmark selection algorithm and a novel approximate similarity matrix approach are designed to construct a sparse similarity matrix within low computational complexities and achieves the lower computational complexity than most existing large-scale spectral clustering methods.

Authors

Hongmin Li

3 papers

Xiucai Ye

2 papers

A. Imakura

2 papers

T. Sakurai

2 papers

TL;DR

Authors

References37 items

IEEE Transactions on Pattern Analysis and Machine Intelligence Publication Information

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On Spectral Clustering: Analysis and an algorithm

Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables

Agglomerative Information Bottleneck

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Her current research interests include feature selection, clustering, bioinformatics, machine learning and its application fields

A survey of kernel and spectral methods for clustering

Uci machine learning repository

Data Mining And Knowledge Discovery Handbook

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He received his MS degree in computer science from the University of Tsukuba, Japan. His current research interests include clustering, machine learning

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