# Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent

arXiv: https://arxiv.org/abs/2310.07166 · HTML: https://mlsanity.com/paper/2310.07166/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering](https://mlsanity.com/paper/2305.06799/) (2023)
- [Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization](https://mlsanity.com/paper/1709.01212/) (2017)
- [Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering](https://mlsanity.com/paper/2203.08060/) (2022)

## Built on by (most cited first, 2 of 2)

- [LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering](https://mlsanity.com/paper/2507.20980/) (2025)
- [One-step Multi-view Clustering With Adaptive Low-rank Anchor-graph Learning](https://mlsanity.com/paper/2509.14724/) (2025)

## Abstract (arXiv)

Multi-view clustering has attracted growing attention owing to its capabilities of aggregating information from various sources and its promising horizons in public affairs. Up till now, many advanced approaches have been proposed in recent literature. However, there are several ongoing difficulties to be tackled. One common dilemma occurs while attempting to align the features of different views. {Moreover, due to the fact that many existing multi-view clustering algorithms stem from spectral clustering, this results to cubic time complexity w.r.t. the number of dataset. However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to tackle the discrepancy among views through hierarchical feature descent and project to a common subspace( STAGE 1), which reveals dependency of different views. We further reduce the computational complexity to linear time cost through a unified sampling strategy in the common subspace( STAGE 2), followed by anchor-based subspace clustering to learn the bipartite graph collectively( STAGE 3). }Extensive experimental results on public benchmark datasets demonstrate that our proposed model consistently outperforms the state-of-the-art techniques.