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We propose an efficient technique to learn probabilistic hierarchical topic models that are designed to preserve the manifold structure of audio data. The consideration of the data manifold is important, as it has been shown to provide superior performance in certain audio applications such as source separation. However, the high computational cost of a sparse encoding step due to the requirement...
We present a deflation method for Nonnegative Matrix Factorization (NMF) that aims to discover latent components one by one in order of importance. To do so we perform a series of individual decompositions, each of which stands for a deflation step. In each deflation we obtain a dominant component and a nonnegative residual, and then the residual is further used as an input to the next deflation in...
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