# Learning A Task-Specific Deep Architecture For Clustering

arXiv: https://arxiv.org/abs/1509.00151 · HTML: https://mlsanity.com/paper/1509.00151/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2015.

## Built on by (most cited first, 6 of 6)

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- [Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks](https://mlsanity.com/paper/2204.11640/) (2022)
- [Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data](https://mlsanity.com/paper/1910.07763/) (2019)
- [Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions](https://mlsanity.com/paper/2411.02558/) (2024)
- [Learning to Structure an Image with Few Colors and Beyond](https://mlsanity.com/paper/2208.08438/) (2022)
- [Improved Representation Learning Through Tensorized Autoencoders](https://mlsanity.com/paper/2212.01046/) (2022)

## Abstract (arXiv)

While sparse coding-based clustering methods have shown to be successful, their bottlenecks in both efficiency and scalability limit the practical usage. In recent years, deep learning has been proved to be a highly effective, efficient and scalable feature learning tool. In this paper, we propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both. A feed-forward network structure, named TAGnet, is constructed based on a graph-regularized sparse coding algorithm. It is then trained with task-specific loss functions from end to end. We discover that connecting deep learning to sparse coding benefits not only the model performance, but also its initialization and interpretation. Moreover, by introducing auxiliary clustering tasks to the intermediate feature hierarchy, we formulate DTAGnet and obtain a further performance boost. Extensive experiments demonstrate that the proposed model gains remarkable margins over several state-of-the-art methods.