# Multilayer Graph Approach to Deep Subspace Clustering

arXiv: https://arxiv.org/abs/2401.17033 · HTML: https://mlsanity.com/paper/2401.17033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 11 of 11)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Deep Subspace Clustering Networks](https://mlsanity.com/paper/1709.02508/) (2017)
- [Subspace Clustering by Block Diagonal Representation](https://mlsanity.com/paper/1805.09243/) (2018)
- [Multi-view Low-rank Sparse Subspace Clustering](https://mlsanity.com/paper/1708.08732/) (2017)
- [A Critique of Self-Expressive Deep Subspace Clustering](https://mlsanity.com/paper/2010.03697/) (2020)
- [Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds](https://mlsanity.com/paper/1303.2221/) (2013)
- [Sketched Subspace Clustering](https://mlsanity.com/paper/1707.07196/) (2017)
- [Structure Learning with Similarity Preserving](https://mlsanity.com/paper/1912.01197/) (2019)
- [Leveraging Union of Subspace Structure to Improve Constrained Clustering](https://mlsanity.com/paper/1608.02146/) (2016)
- [A Data Mining Approach Combining K-Means Clustering with Bagging Neural Network for Short-term Wind Power Forecasting](https://mlsanity.com/paper/1709.05261/) (2017)
- [Symmetric low-rank representation for subspace clustering](https://mlsanity.com/paper/1410.8618/) (2014)

## Built on by (most cited first, 1 of 1)

- [Label-independent hyperparameter-free self-supervised single-view deep subspace clustering](https://mlsanity.com/paper/2504.18179/) (2025)

## Abstract (arXiv)

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.