A multiplicative up-propagation algorithm

Jong-Hoon Ahn, Seungjin Choi, Jong-Hoon Oh
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
more » ... cative up-propagation algorithm is shown. In contrast to NMF, the multiplicative uppropagation is an algorithm that can learn hierarchical representations, where complex higher-level representations are defined in terms of less complex lower-level representations. The interesting behavior of our algorithm is demonstrated with face image data.
doi:10.1145/1015330.1015379 dblp:conf/icml/AhnCO04 fatcat:eylijixhpnbzpgghb32nxre5wy