Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
535490 | Pattern Recognition Letters | 2008 | 7 Pages |
Abstract
This paper proposes a histogram based data-reducing algorithm for improving the performance of the fixed-point independent component analysis (FastICA). This data-reducing independent component analysis (DR-FastICA) is based upon two statistical criteria to keep the histogram contour of processed data. This algorithm uses two steps (a coarse step for data sampling and a fine one for data tuning) to improve the performance of FastICA. Experimental results show that the proposed algorithm can reduce the computation time and implementation memory needed for executing FastICA, especially for large amounts of data (e.g. 1024 × 1024 images).
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Shih-Hsuan Chiu, Chuan-Pin Lu, Dien-Chi Wu, Che-Yen Wen,