Abstract: An adaptive weighted multiple?sparse representation classification method is proposed in this paper. To address the weak discriminative power of the conventional SRC （Sparse Representation Classifier） method which uses a single feature representation, it proposes using multiple features to represent each sample and construct multiple feature sub-dictionaries for classification. To reflect the different importance and discriminative power of each feature, it presents an adaptive weighted method to linearly combine different feature representations for classification. Experimental results demonstrate the effectiveness of the proposed method and better classification accuracy can be obtained than the conventional SRC method.

Key words: adaptive weight, multiple sparse representation, Sparse Representation Classifier（SRC）

摘要： 提出了一种基于多特征字典的稀疏表示算法。该算法针对SRC的单特征鉴别性较弱这一不足，对样本提出多个不同特征并分别进行相应的稀疏表示。并根据SRC算法计算各个特征的鉴别性，自适应地学习出稀疏权重并进行线性加权，从而提高分类的性能。实验表明，基于自适应权重的多重稀疏表示分类算法，具有更好的分类效果。

关键词: 自适应权重, 多重稀疏表示, 稀疏表示分类器（SRC）