Metric Learning with Rank and Sparsity Constraints

来源 :第八届工业与应用数学国际大会 | 被引量 : 0次 | 上传用户：skynet0079

【摘要】

：

　　Choosing a distance preserving metric is fundamental to many signal processing algorithms,such as k-means,nearest neighbor searches,compressive sensing,etc.

【作者】

：

Bah Bubacarr

【机构】

：

Univ.of Texas at Austin

【出处】

：

第八届工业与应用数学国际大会

【发表日期】

：

2015年期

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　　Choosing a distance preserving metric is fundamental to many signal processing algorithms,such as k-means,nearest neighbor searches,compressive sensing,etc.

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Metric Learning with Rank and Sparsity Constraints

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