基于多级结构的深度子空间聚类方法

Deep Subspace Clustering Method Based on the Multi-level Structure

提出了一种新的深度子空间聚类方法,使用了卷积自编码器将输入图像转换为位于线性子空间上的表示。通过结合自编码器提取的低阶和高阶信息来促进特征学习过程,在编码器的不同层级生成多组自我表示和信息表示。将得到的多级信息融合得到统一的系数矩阵并用于后续的聚类。通过多组实验验证了上述创新的有效性,在三个经典数据集:Coil20,ORL和Extended Yale B上,聚类精度分别达到95.38%、87.25%以及97.58%。相较于其他主流方法,能有效提高聚类准确性,并具有较强的鲁棒性。

A new deep subspace clustering method that uses a convolutional autoencoder to transform an input image into a representation that lies on a linear subspace is proposed.The feature learning process is facilitated by combining low-order and high-order information extracted by the autoencoders,and multiple sets of self-representations and information representations are generated at different levels of the encoder.The obtained multi-level information is fused to obtain a unified coefficient matrix and use it for subsequent clustering.The effectiveness of the above innovations is verified through multiple experiments on three classic datasets,including Coil20,ORL and Extended Yale B.And the clustering accuracies reach 95.38%,87.25% and 97.58% respectively.Compared with other mainstream methods,this method can effectively improve the clustering accuracy and it has strong robustness.