Alternating proximal gradient method for sparse nonnegative Tucker decomposition - implementation -
Multi-way data arises in many applications such as electroencephalography
(EEG) classification, face recognition, text mining and hyperspectral data
analysis. Tensor decomposition has been commonly used to find the hidden
factors and elicit the intrinsic structures of the multi-way data. This paper
considers sparse nonnegative Tucker decomposition (NTD), which is to decompose
a given tensor into the product of a core tensor and several factor matrices
with sparsity and nonnegativity constraints. An alternating proximal gradient
method (APG) is applied to solve the problem. The algorithm is then modified to
sparse NTD with missing values. Per-iteration cost of the algorithm is
estimated scalable about the data size, and global convergence is established
under fairly loose conditions. Numerical experiments on both synthetic and real
world data demonstrate its superiority over a few state-of-the-art methods for
(sparse) NTD from partial and/or full observations. The MATLAB code along with
demos are accessible from the author's homepage.
The implementation is on
Yangyang Xu's page.
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