PMSSC: Parallelizable multi-subset based self-expressive model for subspace clustering

Katsuya Hotta, Takuya Akashi, Shogo Tokai, Chao Zhang*

*この論文の責任著者

研究成果: ジャーナルへの寄稿学術論文査読

抄録

Subspace clustering methods which embrace a self-expressive model that represents each data point as a linear combination of other data points in the dataset provide powerful unsupervised learning techniques. However, when dealing with large datasets, representation of each data point by referring to all data points via a dictionary suffers from high computational complexity. To alleviate this issue, we introduce a parallelizable multi-subset based self-expressive model (PMS) which represents each data point by combining multiple subsets, with each consisting of only a small proportion of the samples. The adoption of PMS in subspace clustering (PMSSC) leads to computational advantages because the optimization problems decomposed over each subset are small, and can be solved efficiently in parallel. Furthermore, PMSSC is able to combine multiple self-expressive coefficient vectors obtained from subsets, which contributes to an improvement in self-expressiveness. Extensive experiments on synthetic and real-world datasets show the efficiency and effectiveness of our approach in comparison to other methods. [Figure not available: see fulltext.]

本文言語英語
ページ(範囲)479-494
ページ数16
ジャーナルComputational Visual Media
9
3
DOI
出版ステータス出版済み - 2023/09

ASJC Scopus 主題領域

  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 人工知能

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