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It is an important issue for multi-label classification to discover and utilize data structures or label correlations during the learning process, which could greatly improve the learning performance. In this paper, a unified framework is proposed for multi-label classification by incorporating the supervised low-dimensional embedding into the predictive model. The supervised embedding exploits latent...
This paper presents a generalized incremental Laplacian Eigenmaps (GENILE), a novel online version of the Laplacian Eigenmaps, one of the most popular manifold-based dimensionality reduction techniques which solves the generalized eigenvalue problem. We evaluate the comparative performance of the manifold-based learning techniques using both artificial and real data. Specifically, two popular artificial...
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