Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition

TL;DR

This paper introduces SeekAndDestroy, a novel algorithm for detecting and mitigating concept drift in streaming tensor decompositions, improving robustness and accuracy in dynamic data environments.

Contribution

The paper defines concept drift in streaming tensor decomposition and presents SeekAndDestroy, the first algorithm to detect and alleviate such drift in real-time.

Findings

SeekAndDestroy effectively detects concept drift in synthetic datasets.

The algorithm maintains decomposition quality comparable to full tensor decomposition.

SeekAndDestroy outperforms existing streaming methods on real datasets.

Abstract

Tensor decompositions are used in various data mining applications from social network to medical applications and are extremely useful in discovering latent structures or concepts in the data. Many real-world applications are dynamic in nature and so are their data. To deal with this dynamic nature of data, there exist a variety of online tensor decomposition algorithms. A central assumption in all those algorithms is that the number of latent concepts remains fixed throughout the entire stream. However, this need not be the case. Every incoming batch in the stream may have a different number of latent concepts, and the difference in latent concepts from one tensor batch to another can provide insights into how our findings in a particular application behave and deviate over time. In this paper, we define "concept" and "concept drift" in the context of streaming tensor decomposition, as the manifestation of the variability of latent concepts throughout the stream. Furthermore, we introduce SeekAndDestroy, an algorithm that detects concept drift in streaming tensor decomposition and is able to produce results robust to that drift. To the best of our knowledge, this is the first work that investigates concept drift in streaming tensor decomposition. We extensively evaluate SeekAndDestroy on synthetic datasets, which exhibit a wide variety of realistic drift. Our experiments demonstrate the effectiveness of SeekAndDestroy, both in the detection of concept drift and in the alleviation of its effects, producing results with similar quality to decomposing the entire tensor in one shot. Additionally, in real datasets, SeekAndDestroy outperforms other streaming baselines, while discovering novel useful components.