Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called joint and progressive learning strategy (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by (1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; (2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; (3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
CITATION STYLE
Hong, D., Yokoya, N., Xu, J., & Zhu, X. (2018). Joint and progressive learning from high-dimensional data for multi-label classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11212 LNCS, pp. 478–493). Springer Verlag. https://doi.org/10.1007/978-3-030-01237-3_29
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