Principal Component Analysis [data, math. anal.]

00 preface_ Principal Component Analysis......Page 1
01 Two-Dimensional Principal Component Analysis and Its Extensions......Page 13
02 Application of Principal Component Analysis to Elucidate Experimental and Theoretical Information......Page 35
03 Principal Component Analysis: A Powerful Interpretative Tool at the Service of Analytical Methodology......Page 61
04 Subset Basis Approximation of Kernel Principal Component Analysis......Page 79
05 Multilinear Supervised Neighborhood Preserving Embedding Analysis of Local Descriptor Tensor......Page 103
06 Application of Linear and Nonlinear Dimensionality Reduction Methods......Page 119
07 Acceleration of Convergence of the Alternating Least Squares Algorithm for Nonlinear Principal Components Analysis......Page 141
08 The Maximum Non-Linear Feature Selection of Kernel Based on Object Appearance......Page 157
09 FPGA Implementation for GHA-Based Texture Classification......Page 177
10 The Basics of Linear Principal Components Analysis......Page 193
11 Robust Density Comparison Using Eigenvalue Decomposition......Page 219
12 Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis......Page 235
13 On-Line Monitoring of Batch Process with Multiway PCA/ICA......Page 251
14 Computing and Updating Principal Components......Page 275