# Large-Scale Sparse Subspace Clustering Using Landmarks

## TL;DR

This paper introduces a scalable subspace clustering method that uses landmarks to reduce computational complexity, enabling linear-time clustering on large datasets with demonstrated effectiveness on synthetic and real data.

## Contribution

The paper proposes a landmark-based approach for large-scale sparse subspace clustering, significantly improving efficiency over traditional methods.

## Key findings

- Runs in linear time relative to data size
- Effective on both synthetic and real datasets
- Maintains clustering accuracy with reduced computation

## Abstract

Subspace clustering methods based on expressing each data point as a linear combination of all other points in a dataset are popular unsupervised learning techniques. However, existing methods incur high computational complexity on large-scale datasets as they require solving an expensive optimization problem and performing spectral clustering on large affinity matrices. This paper presents an efficient approach to subspace clustering by selecting a small subset of the input data called landmarks. The resulting subspace clustering method in the reduced domain runs in linear time with respect to the size of the original data. Numerical experiments on synthetic and real data demonstrate the effectiveness of our method.