# Automatic Unsupervised Tensor Mining with Quality Assessment

arXiv: https://arxiv.org/abs/1503.03355 · HTML: https://mlsanity.com/paper/1503.03355/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2015.

## Built on by (most cited first, 4 of 4)

- [Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions](https://mlsanity.com/paper/2209.00322/) (2022)
- [FRAPPE: underlineFast underlineRank underlineApproximation with underlineExplainable Features for Tensors](https://mlsanity.com/paper/2206.09316/) (2022)
- [Multi-Dictionary Tensor Decomposition](https://mlsanity.com/paper/2309.09717/) (2023)
- [Learnable Low-Rank Polynomial Sketch for Effective Linear Attention](https://mlsanity.com/paper/2610.14219/) (2026)

## Abstract (arXiv)

A popular tool for unsupervised modelling and mining multi-aspect data is tensor decomposition. In an exploratory setting, where and no labels or ground truth are available how can we automatically decide how many components to extract? How can we assess the quality of our results, so that a domain expert can factor this quality measure in the interpretation of our results? In this paper, we introduce AutoTen, a novel automatic unsupervised tensor mining algorithm with minimal user intervention, which leverages and improves upon heuristics that assess the result quality. We extensively evaluate AutoTen's performance on synthetic data, outperforming existing baselines on this very hard problem. Finally, we apply AutoTen on a variety of real datasets, providing insights and discoveries. We view this work as a step towards a fully automated, unsupervised tensor mining tool that can be easily adopted by practitioners in academia and industry.