array(2) { ["lab"]=> string(4) "1409" ["publication"]=> string(5) "12660" } Multi-view feature selection via sparse tensor regression - Liang Yong | LabXing

Liang Yong

简介 中藥質量控制資訊系統，生物資訊學

大学 Macau University of Science and Technology / Faculty of Information Technology
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Multi-view feature selection via sparse tensor regression

2021
期刊 International Journal of Wavelets, Multiresolution and Information Processing
In this paper, we propose a sparse tensor regression model for multi-view feature selection. Apart from the most of existing methods, our model adopts a tensor structure to represent multi-view data, which aims to explore their underlying high-order correlations. Based on this tensor structure, our model can effectively select the meaningful feature set for each view. We also develop an iterative optimization algorithm to solve our model, together with analysis about the convergence and computational complexity. Experimental results on several popular multi-view data sets confirm the effectiveness of our model.

  • 页码 2150020
  • World Scientific Publishing Company
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