As far as I have used wavelets are used to smooth or filter the data without dimension reduction . Decomposition and reconstruction of data and then using ML is what I have worked on from what i see it does if you want to make inferences in the frequency domain and PCA helps in solving the curse of dimensionality ...
Thank you very much my dear all friends.
Dr. Aparna Sathya Murthy, you are right. I also used PCA+LSSVM and wavelet+LSSVM and wavelet+PCA+LSSVM. I also found that wavelets are used to smooth or filter the data without dimension reduction . Decomposition and reconstruction of data and then using ML is what I have worked on from what i see it does if you want to make inferences in the frequency domain and PCA helps in solving the curse of dimensionality ...
But I also found that PCA+LSSVM work more efficiently than wavelet+LSSVM.
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