Dictionary-induced least squares framework for multi-view dimensionality reduction with multi-manifold embeddings
IET Computer Vision, pp. 97-108, 2019.
This study proposes a novel dimensionality reduction (DR) method for multi-view datasets. The principal component analysis (PCA) idea of minimising least squares reconstruction errors is extended to consider both data distribution and penalty weights called dictionary to recover outliers free global structures from missing and noisy data ...More
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