一种快速低秩的判别子字典学习算法及图像分类

本文提出了一种快速低秩的判别子字典学习算法。在训练阶段,构造一个子字典的低秩约束项和拉普拉斯矩阵正则化项,加入判别字典学习的目标函数中。将原始样本映射到一个新的空间中,使同一类别的相邻点彼此靠近,同时增强子字典对同类样本的重构能力,针对每类样本的判别性特征,学习出相应的学习字典。在测试阶段,利用k NN分类器估计测试样本的类别标签。同时,将算法应用在3种数据集上,与其他的字典学习算法进行比较,取得了较好的分类结果。

A fast and low-rank discriminant sub-dictionary learning method for image classification

This paper proposes a fast,low-rank discriminative sub-dictionary learning algorithm.In the training phase,the lowrank constraint terms of the sub-dictionary and the Laplacian matrix regularization terms are constructed,and the objective function of the discriminative dictionary learning is added.The original sample is mapped to the new space so that adjacent points of the same category are closed to each other.At the same time,the sub-dictionary’s ability is enhanced to reconstruct similar samples,and the corresponding learning dictionary is learnt according to the discriminative characteristics of each sample.In the testing phase,the kNN classifier is used to estimate the class label of the test sample.Finally,the algorithm are applied to three public data sets compare with other dictionary learning algorithms.The proposed algorithm has achieved better classification results.