Fast Dimensionality Reduction and Simple PCA

Article type: Research Article

Authors: Partridge, Matthew^{a; *} | Calvo, Rafael A.^{b; 1}

Affiliations: [a] Sedal Department of Electrical Engineering, University of Sydney NSW; Sydney 2006, Australia | [b] LTI, School of Computer Science, 4919 Forbes Avenue, Pittsburgh, PA 15213-3702, USA

Correspondence: [*] Corresponding author. E-mail: mp@sedal.usyd.edu.au. URL: http://www.sedal.usyd.edu.au/~mp.

Note: [1] E-mail: rafa@sedal.usyd.edu.au.

Abstract: A fast and simple algorithm for approximately calculating the principal components (PCs) of a dataset and so reducing its dimensionality is described. This Simple Principal Components Analysis (SPCA) method was used for dimensionality reduction of two high-dimensional image databases, one of handwritten digits and one of handwritten Japanese characters. It was tested and compared with other techniques. On both databases SPCA shows a fast convergence rate compared with other methods and robustness to the reordering of the samples.

Keywords: Principal component analysis, Matrix diagonalization, Hebbian learning, Image compression

DOI: 10.3233/IDA-1998-2304

Journal: Intelligent Data Analysis, vol. 2, no. 3, pp. 203-214, 1998