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A multiplicative up-propagation algorithm
2004
Twenty-first international conference on Machine learning - ICML '04
We present a generalization of the nonnegative matrix factorization (NMF), where a multilayer generative network with nonnegative weights is used to approximate the observed nonnegative data. The multilayer generative network with nonnegativity constraints, is learned by a multiplicative uppropagation algorithm, where the weights in each layer are updated in a multiplicative fashion while the mismatch ratio is propagated from the bottom to the top layer. The monotonic convergence of the
doi:10.1145/1015330.1015379
dblp:conf/icml/AhnCO04
fatcat:eylijixhpnbzpgghb32nxre5wy