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Linked CP tensor decomposition algorithms for shared and individual feature extraction
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DOI：10.1016/j.image.2018.11.001.png)
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Tensor decomposition methods are widely used in various areas of science for multilinear feature extraction and dimensionality reduction of multi-way arrays. Linked CANDECOM/PARAFAC (CP) tensor decomposition (LCPTD) can be used for extraction of shared and individual multilinear features from a set of observed multi-way arrays. In many real-world applications, observations are gathered by multiple sensors that might have multiple functionalities. In this study, we propose various algorithmic approaches to estimate shared and individual latent factors in the LCPTD model. Two of them are based on the hierarchical alternating least-squares (HALS) and alternating direction method of multipliers (ADMM) approaches, assuming that both observed arrays can be uploaded to a shared memory. For processing large-scale tensors, we propose a geometry-based computational strategy that aims to distribute computing according to the MapReduce paradigm. The proposed algorithms are validated with synthetically generated and real data. They are applied to feature extraction from the spectrograms of electromyography (EMG) and mechanomyography (MMG) signals that are registered simultaneously by an array of sensors with double functionality. The extracted features are then used for classification of grasping movements for various objects. The HALS-based algorithm is also successfully applied to separation of a moving foreground from a nearly static background in a color video sequence. All the tests demonstrate high efficiency of the proposed approaches, and their usefulness for processing a set of multi-way arrays.
Keyword:
Linked CP tensor decomposition
Algorithms for LCPTD
Feature fusion
EMG/MMG signal classification
Video sequence modeling
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