Deep subspace clustering (DSC) networks based on self-expressive model learn
representation matrix, often implemented in terms of fully connected network,
in the embedded space. After the learning is finished, representation matrix is
used by spectral clustering module to assign labels to clusters. However, such
approach ignores complementary information that exist in other layers of the
encoder (including the input data themselves). Herein, we apply selected linear
subspace clustering algorithm to learn representation matrices from
representations learned by all layers of encoder network including the input
data. Afterward, we learn a multilayer graph that in a multi-view like manner
integrates information from graph Laplacians of all used layers. That improves
further performance of selected DSC network. Furthermore, we also provide
formulation of our approach to cluster out-of-sample/test data points. We
validate proposed approach on four well-known datasets with two DSC networks as
baseline models. In almost all the cases, proposed approach achieved
statistically significant improvement in three performance metrics. MATLAB code
of proposed algorithm is posted on this https URL

深度子空间聚类网络通过线性子空间聚类算法学习表示矩阵，进一步改进了多层图的性能，并在聚类外样本数据点中取得了统计学上显著的改进。