{"type":"ScholarlyArticle","title":"Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning","authors":[{"name":"Zhao Zhang","slug":"zhao-zhang","given":"Zhao","family":"Zhang","affiliation":"School of Computer Science & School of Artificial Intelligence , Hefei University of Technology , China"},{"name":"Weiming Jiang","slug":"weiming-jiang","given":"Weiming","family":"Jiang","affiliation":"School of Computer Science and Technology , Soochow University , China"},{"name":"Zheng Zhang","slug":"zheng-zhang","given":"Zheng","family":"Zhang","affiliation":"School of Information Technology and Electrical Engineering , University of Queensland , Australia"},{"name":"Sheng Li","slug":"sheng-li","given":"Sheng","family":"Li","affiliation":"Department of Computer Science , University of Georgia , USA"},{"name":"Guangcan Liu","slug":"guangcan-liu","given":"Guangcan","family":"Liu","affiliation":"School of Information and Control , Nanjing University of Information Science and Technology , China"},{"name":"Jie Qin","slug":"jie-qin","given":"Jie","family":"Qin","affiliation":"Computer Vision Laboratory , ETH Zurich , Switzerland"}],"abstract":"We propose a novel structured discriminative block- diagonal dictionary learning method, referred to as scalable Locality-Constrained Projective Dictionary Learning (LC-PDL), for efficient representation and classification. To improve the scalability by saving both training and testing time, our LC-PDL aims at learning a structured discriminative dictionary and a block-diagonal representation without using costly l0/l1-norm. Besides, it avoids extra time-consuming sparse reconstruction process with the well-trained dictionary for new sample as many existing models.  More importantly, LC-PDL avoids using the com- plementary data matrix to learn the sub-dictionary over each class. To enhance the performance, we incorporate a locality constraint of atoms into the DL procedures to keep local information and obtain the codes of samples over each class separately. A block-diagonal discriminative approximation term is also derived to learn a discriminative projection to bridge data with their codes by extracting the special block-diagonal features from data, which can ensure the approximate coefficients to associate with its label information clearly. Then, a robust multiclass classifier is trained over extracted block-diagonal codes for accurate label predictions. Experimental results verify the effectiveness of our algorithm.","doi":"10.24963/ijcai.2019/608","url":"https://ijcai.org/proceedings/2019/608","pdf":"https://ijcai.org/proceedings/2019/0608.pdf","markdown":"https://ijcai.org/proceedings/2019/608.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/608","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":608,"pages":{"first":4376,"last":4382},"keywords":["Machine Learning: Feature Selection ; Learning Sparse Models","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Machine Learning Applications: Applications of Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Aharon et al., 2006] Michal Aharon, Michal Elad, Alfred Bruckstein and Yana Katz. 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