Kernel Near Principal Component Analysis

Technical Report · Mon Jul 01 00:00:00 EDT 2002

DOI:https://doi.org/10.2172/810934· OSTI ID:810934

MARTIN, SHAWN B

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.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
• Sponsoring Organization: US Department of Energy (US)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 810934
• Report Number(s): SAND2001-3769; TRN: US200311%%168
• Resource Relation: Other Information: PBD: 1 Jul 2002
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ALGORITHMS
DATA ANALYSIS
FLUID FLOW
KERNELS
VECTORS