# Subspace Clustering of Very Sparse High-Dimensional Data

arXiv: https://arxiv.org/abs/1901.09108 · HTML: https://mlsanity.com/paper/1901.09108/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2019.

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

- [Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning](https://mlsanity.com/paper/2106.04330/) (2021)

## Abstract (arXiv)

In this paper we consider the problem of clustering collections of very short texts using subspace clustering. This problem arises in many applications such as product categorisation, fraud detection, and sentiment analysis. The main challenge lies in the fact that the vectorial representation of short texts is both high-dimensional, due to the large number of unique terms in the corpus, and extremely sparse, as each text contains a very small number of words with no repetition. We propose a new, simple subspace clustering algorithm that relies on linear algebra to cluster such datasets. Experimental results on identifying product categories from product names obtained from the US Amazon website indicate that the algorithm can be competitive against state-of-the-art clustering algorithms.