# Residual Encoder-Decoder Network for Deep Subspace Clustering

arXiv: https://arxiv.org/abs/1910.05569 · HTML: https://mlsanity.com/paper/1910.05569/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Learning a Self-Expressive Network for Subspace Clustering](https://mlsanity.com/paper/2110.04318/) (2021)
- [Self-Supervised Deep Subspace Clustering with Entropy-norm](https://mlsanity.com/paper/2206.04958/) (2022)
- [Learning Structure Aware Deep Spectral Embedding](https://mlsanity.com/paper/2305.08215/) (2023)
- [Federated Deep Subspace Clustering](https://mlsanity.com/paper/2501.00230/) (2024)

## Abstract (arXiv)

Subspace clustering aims to cluster unlabeled data that lies in a union of low-dimensional linear subspaces. Deep subspace clustering approaches based on auto-encoders have become very popular to solve subspace clustering problems. However, the training of current deep methods converges slowly, which is much less efficient than traditional approaches. We propose a Residual Encoder-Decoder network for deep Subspace Clustering (RED-SC), which symmetrically links convolutional and deconvolutional layers with skip-layer connections, with which the training converges much faster. We use a self-expressive layer to generate more accurate linear representation coefficients through different latent representations from multiple latent spaces. Experiments show the superiority of RED-SC in training efficiency and clustering accuracy. Moreover, we are the first one to apply residual encoder-decoder on unsupervised learning tasks.