Multi-view unsupervised feature selection with adaptive similarity and view weight

Chenping Hou, Feiping Nie, Hong Tao, Dongyun Yi

Research output: Contribution to journalArticlepeer-review

169 Scopus citations

Abstract

With the advent of multi-view data, multi-view learning has become an important research direction in both machine learning and data mining. Considering the difficulty of obtaining labeled data in many real applications, we focus on the multi-view unsupervised feature selection problem. Traditional approaches all characterize the similarity by fixed and pre-defined graph Laplacian in each view separately and ignore the underlying common structures across different views. In this paper, we propose an algorithm named Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight (ASVW) to overcome the above mentioned problems. Specifically, by leveraging the learning mechanism to characterize the common structures adaptively, we formulate the objective function by a common graph Laplacian across different views, together with the sparse ℓ2,p-norm constraint designed for feature selection. We develop an efficient algorithm to address the non-smooth minimization problem and prove that the algorithm will converge. To validate the effectiveness of ASVW, comparisons are made with some benchmark methods on real-world datasets. We also evaluate our method in the real sports action recognition task. The experimental results demonstrate the effectiveness of our proposed algorithm.

Original languageEnglish
Article number7876761
Pages (from-to)1998-2011
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume29
Issue number9
DOIs
StatePublished - 1 Sep 2017

Keywords

  • adaptive similarity and view weight
  • multiple view data mining
  • sports action recognition
  • unsupervised feature selection

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