【摘 要】
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Multi-view classification and feature selection have received considerable attention in recent years.In many real classification problems,the data in each view may have noise.The low-rank regression m
【出 处】
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第二届中国计算机学会生物信息学会议
论文部分内容阅读
Multi-view classification and feature selection have received considerable attention in recent years.In many real classification problems,the data in each view may have noise.The low-rank regression model has been proved and applied to capture underlying classes correlation patterns,such that the classification results can be enhanced.In order to make it sparse,in this paper,we propose a novel method for sparse multi-view low-rank regression(SMLLR)and sparse multi-view full-rank regression(SMFRR).The method based on sparse theory is to make the matrix decomposition produce sparse results by adding the penalty factors in the matrix transformation process.
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