判别式
计算机科学
地点
人工智能
对角线的
可扩展性
子空间拓扑
词典学习
块(置换群论)
分类器(UML)
模式识别(心理学)
分块矩阵
理论计算机科学
算法
稀疏逼近
数学
几何学
物理
数据库
哲学
量子力学
特征向量
语言学
作者
Zhao Zhang,Weiming Jiang,Zheng Zhang,Sheng Li,Guangcan Liu,Jie Qin
标识
DOI:10.24963/ijcai.2019/608
摘要
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.
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