2018/08/15 by Danfeng Hong, Hong, Danfeng, Naoto Yokoya +6
Computer Science · Mathematics · #Artificial intelligence #Computer science #Dimensionality reduction #Discriminative model #Domain Adaptation and Few-Shot Learning #Embedding #FOS: Computer and information sciences #Face and Expression Recognition #Feature (linguistics) #Feature learning #Feature vector #Generalization #Linearization #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Manifold (fluid mechanics) #Mathematical analysis #Mathematics #Nonlinear dimensionality reduction #Nonlinear system #Pattern recognition (psychology) #Physics #Random subspace method #Representation (politics) #Subspace topology #Text and Document Classification Technologies #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1808.05110
accepted in ECCV 2018
arxiv created 2018/08/15 · openalex publication_date 2018/08/15 · arxiv updated 2018/08/16 · openalex created_date 2019/06/27 · openalex updated_date 2026/08/05
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.