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 signals from the real-world database. These are compared with three commonly-adopted feature extraction techniques using five classes classification tasks related to ECG heartbeats. The AUC(Area under the ROC curve) of the PCCA-based feature extraction technique with two-digits size train dataset for four ECG type-pairs we compared were 0.8805, 0.957, 0.8968 and 1.00 respectively. The experimental results demonstrate that the proposed feature extraction techniques achieve better performance compared to other features extraction techniques with small amount of well-labeled data.