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 complementary 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.

提出了一个新的结构判别性分块对角线字典学习方法，称为可扩展的局部限制投影字典学习（LC-PDL），用于有效的表示和分类，通过学习结构化的判别性字典和分块对角线表示来提高可扩展性，并避免利用昂贵的I0/I1范数等，实现了省略新样本的稀疏重建过程，更重要的是避免使用补充数据矩阵来学习每个类别的子字典，该方法还在DL过程中加入原子的局部性约束来保留局部信息，从而获得样本在每个类别上的编码，并推导出了一个分块对角线的判别逼近项来学习判别性投影，以从数据中提取出特殊的分块对角线特征来确保近似系数与其标签信息相关联，最后，还训练了一个强健的多类分类器，用于精确的标签预测。

可扩展的块对角线局部约束投影字典学习