Improving the Efficiency of the Support Vector Decomposition Machine

被引：0

作者：

Tadic, Predrag R. ^{[1
]}

Asadi, Nima ^{[2
]}

Popovic, Nikola ^{[1
]}

Obradovic, Zoran ^{[3
,4
]}

机构：

[1] Univ Belgrade, Sch Elect Engn, Bul Kralja Aleksandra 73, Belgrade 11120, Serbia

[2] Temple Univ, Comp & Informat Sci Dept, Philadelphia, PA 19122 USA

[3] Temple Univ, Ctr Data Analyt & Biomed Informat, Comp & Informat Sci Dept, Philadelphia, PA 19122 USA

[4] Temple Univ, Stat Sci Dept, Philadelphia, PA 19122 USA

来源：

2018 14TH SYMPOSIUM ON NEURAL NETWORKS AND APPLICATIONS (NEUREL) | 2018年

关键词：

Feature selection; Supervised dimensionality reduction; Support vector decomposition machine; DIMENSIONALITY REDUCTION; PCA;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Support Vector Decomposition Machine is a supervised dimensionality reduction technique which simultaneously minimizes reconstruction error and classification loss. To guarantee a unique minimum, a set of arbitrary constraints are introduced. We propose a different set of constraints, which result in a much more efficient implementation, drastically reducing both training and inference time in simulations with synthetic data.

引用

页数：4

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