Magazine Article

Method of Real-Time Principal-Component Analysis

TBMG-149

01/01/2005

Abstract
Content

Dominant element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.

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Citation
"Method of Real-Time Principal-Component Analysis," Mobility Engineering, January 1, 2005.
Additional Details
Publisher
Published
Jan 1, 2005
Product Code
TBMG-149
Content Type
Magazine Article
Language
English