Abstract
PCA (principal component analysis) is a wellknown statistical technique used in many signal processing applications. An on-line temporal PCA learning algorithm is implemented on a floating-point DSP for real-time applications. This algorithm is coded in assembly language to optimize. The experimental results showed that the implemented on-line temporal PCA algorithm not only can accurately estimate the principal components from the input but also can track the principal components from the time varying input. And this algorithm can be applied in space easily by using spacial signals as its inputs instead of using the past inputs as in temporal PCA.
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