Neural Nonnegative CP Decomposition for Hierarchical Tensor Analysis

Abstract

There is a significant demand for topic modeling on large-scale data with complex multi-modal structure in applications such as multi-layer network analysis, temporal document classification, and video data analysis; frequently this multi-modal data has latent hierarchical structure. We propose a new hierarchical nonnegative CANDECOMP/PARAFAC (CP) decomposition (hierarchical NCPD) model and a training method, Neural NCPD, for performing hierarchical topic modeling on multi-modal tensor data. Neural NCPD utilizes a neural network architecture and backpropagation to mitigate error propagation through hierarchical NCPD.

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Reproductions