Machine Learning Way for Boosting Accuracy in Canonical Correlation Analysis based Frequency Recognition

Canonical Correlation Analysis (CCA) is used to frequency recognition of multichannel signals. The unknown signals are compared against known templates and their frequencies are recognized by simply comparing the biggest coefficients of their CCA coefficient vectors. This strategy is straightforward but may not give optimal results. To boost the accuracy of recognition we reformulate the approach in views of machine learning. In this paper, we propose a new strategy based on supervised learning. We also employ feature selection within this framework to adopt efficient coefficients which may not be the largest coefficients for the features vectors. The recognition method is validated by results with real world data.