An integrated optimisation algorithm for feature extraction, dictionary learning and classification

Yan Cui

doi:10.1016/j.neucom.2017.11.043

An integrated optimisation algorithm for feature extraction, dictionary learning and classification

2018-01-01

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DOI：10.1016/j.neucom.2017.11.043

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Recently, sparse representation-based classification (SRC) has received much attention for its robustness in pattern recognition. Because SRC deals with high-dimensional data, huge computational resources are required to compute sparse representations of query samples, which renders SRC solutions of high-dimensional problems infeasible. To overcome this problem, an integrated optimisation algorithm is proposed to implement feature extraction, dictionary learning and classification simultaneously. First, to obtain sparse representation coefficients, a sparsity preserving embedding map is learnt to reduce the dimensionality of the data. Second, an optimal dictionary is adaptively obtained from the training data to reduce trivial information. Third, the training samples are reclassified using sparse representation coefficients. Furthermore, the integrated learning algorithm is extended to unsupervised learning. Experimental results clearly demonstrate that the proposed method achieves better performance than several popular feature extraction and classification methods. (C) 2017 Elsevier B.V. All rights reserved.

Keyword:

Feature extraction

Dictionary optimisation

Sparse representation

Supervised learning

Unsupervised learning

An integrated optimisation algorithm for feature extraction, dictionary learning and classification

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