# Online Sparse Subspace Clustering

arXiv: https://arxiv.org/abs/1902.10842 · HTML: https://mlsanity.com/paper/1902.10842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

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

- [Online Graph Learning under Smoothness Priors](https://mlsanity.com/paper/2103.03762/) (2021)

## Abstract (arXiv)

This paper focuses on the sparse subspace clustering problem, and develops an online algorithmic solution to cluster data points on-the-fly, without revisiting the whole dataset. The strategy involves an online solution of a sparse representation (SR) problem to build a (sparse) dictionary of similarities where points in the same subspace are considered "similar," followed by a spectral clustering based on the obtained similarity matrix. When the SR cost is strongly convex, the online solution converges to within a neighborhood of the optimal time-varying batch solution. A dynamic regret analysis is performed when the SR cost is not strongly convex.