approach make sense
Does my machine learning approach make sense ?
I have multivariate time series data from sensors. I want to feed the raw, unlabeled data into a deep learning model (Thinking of deep belief nets right now). I hope that in the output layer of the rbm, there will be features of the time series. With every additional rbm, i will learn features of higher level . Those features will be used to represent the time series.