Deep Dictionary Learning with An Intra-class Constraint

Autor:	Yuan, Xia, Gou, Jianping, Yu, Baosheng, Yu, Jiali, Yi, Zhang
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	In recent years, deep dictionary learning (DDL)has attracted a great amount of attention due to its effectiveness for representation learning and visual recognition.~However, most existing methods focus on unsupervised deep dictionary learning, failing to further explore the category information.~To make full use of the category information of different samples, we propose a novel deep dictionary learning model with an intra-class constraint (DDLIC) for visual classification. Specifically, we design the intra-class compactness constraint on the intermediate representation at different levels to encourage the intra-class representations to be closer to each other, and eventually the learned representation becomes more discriminative.~Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage. Experimental results on four image datasets show that our method is superior to the state-of-the-art methods. Comment: 6 pages, 3 figures, 2 tables. It has been accepted in ICME2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.06841 Zobrazit plný text záznamu View this record from Arxiv