矩阵分解理论在降维中的应用  

The Applications of Matrix Decomposition Theory for Dimensionality Reduction

作　　者：李明[1] 

机构地区：[1]辽宁师范大学数学学院,辽宁大连116029

出　　处：《吉林师范大学学报（自然科学版）》2010年第3期117-119,122,共4页Journal of Jilin Normal University:Natural Science Edition

摘　　要：本文主要介绍矩阵论中的矩阵分解在计算机人工智能中的降维中的应用.从矩阵的奇异值分解和张量的高阶奇异值分解两个方面,结合张量子空间分析(TSA)和张量邻域保持嵌入(TNPE)两个算法,研究矩阵分解理论与降维的结合及应用原理.In this paper, we mainly introduce the applications of matrix decomposition in Matrix Theory for dimensionality reduction in machine learning of artificial intelligence. Combined with two algorithms of Tensor Subspace Analysis and Tensor Neighborhood Preserving Embedding, we discuss the theory of applications for dimensionality reduction based on the singular value decomposition of matrix and higher-order singular value decomposition of tensor.

关 键 词：降维 奇异值分解 高阶奇异值分解 

分 类 号：O24[理学—计算数学]