Fast Clustering With Anchor Guidance

Feiping Nie, Jingjing Xue, Weizhong Yu, Xuelong Li

科研成果: 期刊稿件文章同行评审

18 引用 (Scopus)

摘要

Clustering aims to partition a set of objects into different groups through the internal nature of these objects. Most existing methods face intractable hyper-parameter problems triggered by various regularization terms, which degenerates the applicability of models. Moreover, traditional graph clustering methods always encounter the expensive time overhead. To this end, we propose a Fast Clustering model with Anchor Guidance (FCAG). The proposed model not only avoids trivial solutions without extra regularization terms, but is also suitable to deal with large-scale problems by utilizing the prior knowledge of the bipartite graph. Moreover, the proposed FCAG can cope with out-of-sample extension problems. Three optimization methods Projected Gradient Descent (PGD) method, Iteratively Re-Weighted (IRW) algorithm and Coordinate Descent (CD) algorithm are proposed to solve FCAG. Extensive experiments verify the superiority of the optimization method CD. Besides, compared with other bipartite graph models, FCAG has the better performance with the less time cost. In addition, we prove through theory and experiment that when the learning rate of PGD tends to infinite, PGD is equivalent to IRW.

源语言英语
页(从-至)1898-1912
页数15
期刊IEEE Transactions on Pattern Analysis and Machine Intelligence
46
4
DOI
出版状态已出版 - 1 4月 2024

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