Automatic Unsupervised Tensor Mining with Quality Assessment

TL;DR

AutoTen is an innovative unsupervised tensor mining algorithm that automatically determines the number of components and assesses result quality, enabling easier and more reliable multi-aspect data analysis without labels.

Contribution

The paper introduces AutoTen, the first automatic tensor mining method that minimizes user intervention and improves result quality assessment in unsupervised settings.

Findings

AutoTen outperforms existing baselines on synthetic data.

AutoTen provides meaningful insights on real datasets.

The method enables fully automated tensor analysis.

Abstract

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.