CAJ | 학술논문

提出了一种残差加权的多元素协同表示算法。该算法针对SRC的单一鉴别性不足，对样本提出样本与字典的多元素分解并分别进行相应的协同表示，自适应地学习出多元素的残差权重并进行线性加权，从而提高分类的性能。实验表明：自适应残差加权的多元素协同表示分类算法，能够有效提高识别性能。
제출료일충잔차가권적다원소협동표시산법。해산법침대SRC적단일감별성불족，대양본제출양본여자전적다원소분해병분별진행상응적협동표시，자괄응지학습출다원소적잔차권중병진행선성가권，종이제고분류적성능。실험표명：자괄응잔차가권적다원소협동표시분류산법，능구유효제고식별성능。
An adaptive weighted residuals multi-element collaborative representation classification is proposed in this paper. To address the weak discriminative power of SRC (sparse representation classifier) method, we propose using multiple elements to represent each element and construct multiple collaborative representation for classification. To reflect the different element with different importance and discriminative power, we present adaptive weighted residuals method to linearly combine different element representations for classification. Experimental results demonstrate the effectiveness and better classification accuracy of our proposed method.