.png){kind=icon}
返回
Supervised dimensionality reduction via sequential semidefinite programming
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2008.06.015.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many dimensionality reduction problems end up with a trace quotient formulation. Since it is difficult to directly solve the trace quotient problem, traditionally the trace quotient cost function is replaced by an approximation such that the generalized eigenvalue decomposition can be applied. In contrast, we directly optimize the trace quotient in this work. It is reformulated as a quasi-linear semidefinite optimization problem, which can be solved globally and efficiently using standard off-the-shelf semidefinite programming solvers. Also this optimization strategy allows one to enforce additional constraints (for example. sparseness constraints) on the projection matrix. We apply this optimization framework to a novel dimensionality reduction algorithm. The performance of the proposed algorithm is demonstrated in experiments by several UCI machine learning benchmark examples, USPS handwritten digits as well as ORL and Yale face data. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
dimensionality reduction
semidefinite programming
linear discriminant analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
Linear Multi-Degree-of-Freedom System Stochastic Response by Using the Harmonic Wavelet Transform
PREAI
分享收藏A Review of Technical Impact of Electrical Vehicle Charging Stations on Distribution Grid电动汽车充电站对配电网的技术影响研究综述
PREAI
分享收藏
Phase I study of ispinesib in combination with carboplatin in patients with advanced solid tumors
PREAI
分享收藏分享收藏
查看更多内容