Parametric canonical correlation analysis

Abstract

Generally, suppose a wave is a linear combination of multiple basis(Not necessarily a sine or cosine waves, it could also be a wavelet, etc.), different types of waves may be similar on some basis, but vary greatly on a certain basis. To address this problem, we introduce a PCCA-based feature extraction method that extends canonical correlation analysis (CCA). The PCCA-based method can train efficient classifiers to rely on only a few samples for periodic signals with support for removing noisy signals. As a demonstration, an efficient system is implemented for the classification of electrocardiogram (ECG) signals by PCCA. The performance is measured using several normal and abnormal ECG sig..