WaveSim Transform for Multi-channel Signal Data Mining Through Linear Regression PCA
Temporal data mining is concerned with the analysis of temporal data and finding temporal patterns, regularities, trends, clusters in sets of temporal data. In this paper we extract regression features from the coefficients obtained by applying WaveSim Transform on Multi-Channel signals. WaveSim Transform is a reverse approach for generating Wavelet Transform like coefficients by using a conventional similarity measure between the function f(t) and the wavelet. WaveSim transform provides a means to analyze a temporal data at multiple resolutions. We propose a method for computing principal components when the feature is of linear regression type i.e. a line. The resultant principal component features are also lines. So through PCA we achieve dimensionality reduction and thus we show that from the first few principal component regression lines we can achieve a good classification of the objects or samples. The techniques have been tested on an EEG dataset recorded through 64 channels and the results are very encouraging.
KeywordsDimensionality Reduction Dynamic Time Warping Haar Wavelet Temporal Database Multiple Resolution
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