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A generalized multi-dictionary least squares framework regularized with multi-graph embeddings
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DOI：10.1016/j.patcog.2019.01.012.png)
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Dimensionality reduction in high dimensional multi-view datasets is an important research topic. It can keep essential features to improve performance in subsequent tasks such as classification and clustering. This paper proposes a generalized framework, which extends the PCA idea of minimizing least squares reconstruction errors, to include data distribution and multiple dictionaries for preserving outliers-free global structures in multi-view datasets. To also preserve local manifold structures, multiple local graphs are incorporated. Finally two models, in Multi-dictionary Least Squares Framework regularized with Multi-graph Embeddings (MD-MGE), are proposed for preserving both global and local structures. Extensive experimental results on four multi-view datasets prove both methods outperform the existing comparative methods. Also, their accuracy rates improvements are statistically significant on all cases below the significance level of 0.05. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Multi-view dimension reduction
Least squares
Multiple graphs
Feature extraction
Classification
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