科技报告详细信息
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 |
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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.
【 预 览 】
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810934.pdf | 14115KB | download |