College of Big Data and Software Engineering, Zhejiang Wanli University, Ningbo 315100, China.
College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo 315175, China.
Comput Intell Neurosci. 2022 Sep 30;2022:2551137. doi: 10.1155/2022/2551137. eCollection 2022.
Big data has the traits such as "the curse of dimensionality," high storage cost, and heavy computation burden. Self-representation-based feature extraction methods cannot effectively deal with the image-level structural noise in the data, so how to character a better relationship of reconstruction representation is very important. Recently, sparse representation with smoothed matrix multivariate elliptical distribution (SMED) using structural information to handle low-rank error images caused by illumination or occlusion has been proposed. Based on SMED, we present a new method named SMEDP for feature extraction. SMEDP firstly utilizes SMED to automatically construct an adjacency graph and then obtains an optimal projection matrix by maximizing the ratio of the local scatter matrix and the total scatter matrix in the PCA subspace. Experiments on the COIL-20 object database, ORL face database, and CMU PIE face database prove that SMEDP works well and can achieve considerable visual and recognition performance than the relevant methods.
大数据具有“维度诅咒”、存储成本高和计算负担重等特点。基于自表示的特征提取方法不能有效地处理数据中的图像级结构噪声，因此如何刻画更好的重建表示关系非常重要。最近，提出了一种利用结构信息处理光照或遮挡引起的低秩误差图像的基于平滑矩阵多元椭圆分布（SMED）的稀疏表示方法。基于 SMED，我们提出了一种新的特征提取方法 SMEDP。SMEDP 首先利用 SMED 自动构建邻接图，然后通过最大化 PCA 子空间中局部散射矩阵与总散射矩阵的比值来获得最优投影矩阵。在 COIL-20 物体数据库、ORL 人脸数据库和 CMU PIE 人脸数据库上的实验证明，SMEDP 效果良好，与相关方法相比，能够实现相当可观的视觉和识别性能。
