Kernel Near Principal Component Analysis

Martin, Shawn B.

Sandia National Laboratories

关键词: Data Analysis;    Vectors;    99 General And Miscellaneous//Mathematics, Computing, And Information Science;    Fluid Flow;    Kernels;   

DOI  :  10.2172/810934
RP-ID  :  SAND2001-3769
RP-ID  :  AC04-94AL85000
RP-ID  :  810934

美国|英语

来源: UNT Digital Library

【 摘 要 】

We propose a novel algorithm based on Principal Component Analysis (PCA). First, we present an interesting approximation of PCA using Gram-Schmidt orthonormalization. Next, we combine our approximation with the kernel functions from Support Vector Machines (SVMs) to provide a nonlinear generalization of PCA. After benchmarking our algorithm in the linear case, we explore its use in both the linear and nonlinear cases. We include applications to face data analysis, handwritten digit recognition, and fluid flow.