Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition

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.Comment: 16 Pages, Accepted at ECML-PKDD 201

Converse Smith-Martin cell cycle kinetics by transformed B lymphocytes

Recent studies using direct live cell imaging have reported that individual B lymphocytes have correlated transit times between their G1 and S/G2/M phases. This finding is in contradiction with the influential model of Smith and Martin that assumed the bulk of the total cell cycle time variation arises in the G1 phase of the cell cycle with little contributed by the S/G2/M phase. Here we extend these studies to examine the relation between cell cycle phase lengths in two B lymphoma cell lines. We report that transformed B lymphoma cells undergo a short G1 period that displays little correlation with the time taken for the subsequent S/G2/M phase. Consequently, the bulk of the variation noted for total division times within a population is found in the S/G2/M phases and not the G1 phase. Models that reverse the expected source of variation and assume a single deterministic time in G1 followed by a lag + exponential distribution for S/G2/M fit the data well. These models can be improved further by adopting two sequential distributions or by using the stretched lognormal model developed for primary lymphocytes. We propose that shortening of G1 transit times and uncoupling from other cell cycle phases may be a hallmark of lymphocyte transformation that could serve as an observable phenotypic marker of cancer evolution.K. Pham, A. Kan, L. Whitehead, R. J. Hennessy, K. Rogers, P. D. Hodgki

Online data fusion using incremental tensor learning

© Springer Nature Switzerland AG 2019. Despite the advances in Structural Health Monitoring (SHM) which provides actionable information on the current and future states of infrastructures, it is still challenging to fuse data properly from heterogeneous sources for robust damage identification. To address this challenge, the sensor data fusion in SHM is formulated as an incremental tensor learning problem in this paper. A novel method for online data fusion from heterogeneous sources based on incrementally-coupled tensor learning has been proposed. When new data are available, decomposed component matrices from multiple tensors are updated collectively and incrementally. A case study in SHM has been developed for sensor data fusion and online damage identification, where the SHM data are formed as multiple tensors to which the proposed data fusion method is applied, followed by a one-class support vector machine for damage detection. The effectiveness of the proposed method has been validated through experiments using synthetic data and data obtained from a real-life bridge. The results have demonstrated that the proposed fusion method is more robust to noise, and able to detect, assess and localize damage better than the use of individual data sources