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 https://github.com/lovro-sinda/MLG-DSC.

翻译：基于自表达模型的深度子空间聚类（DSC）网络在嵌入空间中学习表示矩阵，该矩阵通常通过全连接网络实现。完成学习后，表示矩阵由谱聚类模块用于分配聚类标签。然而，此类方法忽视了编码器其他层（包括输入数据本身）中存在的互补信息。本文中，我们应用选定的线性子空间聚类算法，从编码器网络所有层（含输入数据）学习的表示中提取表示矩阵。随后，我们以多视图方式构建多层图，整合所有使用层的图拉普拉斯信息，从而进一步提升了选定DSC网络的性能。此外，我们还给出了对测试集/样本外数据点进行聚类的实现方案。我们以两种DSC网络为基准模型，在四个知名数据集上验证了所提方法。在绝大多数案例中，该方法在三个性能指标上取得了统计显著的提升。所提算法的MATLAB代码已发布在https://github.com/lovro-sinda/MLG-DSC。