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CORR
2010
Springer
320views Education» more  CORR 2010»
13 years 9 months ago
An algorithm for the principal component analysis of large data sets
Recently popularized randomized methods for principal component analysis (PCA) efficiently and reliably produce nearly optimal accuracy -- even on parallel processors -- unlike the...
Nathan Halko, Per-Gunnar Martinsson, Yoel Shkolnis...
NECO
2011
13 years 3 months ago
Least-Squares Independent Component Analysis
Accurately evaluating statistical independence among random variables is a key element of Independent Component Analysis (ICA). In this paper, we employ a squared-loss variant of ...
Taiji Suzuki, Masashi Sugiyama
ICASSP
2010
IEEE
13 years 3 months ago
Modified hierarchical clustering for sparse component analysis
The under-determined blind source separation (BSS) problem is usually solved using the sparse component analysis (SCA) technique. In SCA, the BSS is usually solved in two steps, w...
Nasser Mourad, James P. Reilly
ICDM
2006
IEEE
225views Data Mining» more  ICDM 2006»
14 years 3 months ago
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
ISNN
2004
Springer
14 years 2 months ago
Progressive Principal Component Analysis
Abstract. Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best r...
Jun Liu, Songcan Chen, Zhi-Hua Zhou