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A generalized multi-dictionary least squares framework regularized with multi-graph embeddings

Timothy Apasiba Abeo,Xiang-jun Shen,Bingkun Bao,Zhengjun Zha,Jianping Fan

Published 2019 in Pattern Recognition

ABSTRACT

Abstract 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.

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