The lunch will be served 12:00, while the talk starts at 12:15.
Speaker: Evrim Acar Ataman (Head of department, chief research scientist/research professor at Simula Research Laboratory)
Location: UiO (8th floor of Niels Henrik Abels hus) and Zoom.
Title: Constrained Multimodal Data Mining.
Abstract: In order to understand complex systems such as the human metabolome (i.e., the complete set of small biochemical compounds in the body) or the brain, the system should be recorded using different sensing technologies. This creates a surge of data at an unprecedented complexity, and main pillars of the complexity are heterogeneous multimodal data sets, some of which evolve in time while others are static. For instance, measurements of blood samples collected at multiple time points form a dynamic metabolomics data set showing how metabolites change in time, and can be represented as a multi-way array (also referred to as a higher-order tensor) with one of the modes corresponding to time, e.g., people by metabolites by time. This temporal data can be coupled with other data sets such as genetics or gut microbiome data in the form of people by features matrices. There is an emerging need to jointly analyze such heterogeneous multimodal data sets and capture the underlying patterns. While coupled matrix and tensor factorizations (CMTF) are effective approaches for multimodal data mining, there are still various challenges, in particular, in terms of capturing the underlying patterns and their evolution in time. In this talk, we first introduce a flexible algorithmic framework relying on Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM) in order to facilitate the use of a variety of constraints, loss functions and couplings with linear transformations when fitting CMTF models. Numerical experiments on simulated and real data demonstrate that the proposed AO-ADMM-based approach is accurate, flexible and computationally efficient with comparable or better performance than available CMTF algorithms. We then discuss the extension of the framework to joint analysis of dynamic and static data sets by incorporating alternative tensor factorization approaches, which have shown promising performance in terms of revealing evolving patterns in temporal data analysis.
This talk will be mainly based on the following papers and ongoing work in the TrACEr project:
C. Schenker, X. Wang, and E. Acar. PARAFAC2-based Coupled Matrix and Tensor Factorizations, arXiv:2210.13054, 2022
M. Roald, C. Schenker, V. D. Calhoun, T. Adali, R. Bro, J. E. Cohen, and E.Acar. An AO-ADMM Approach to Constraining PARAFAC2 on All Modes, SIAM Journal on Mathematics of Data Science, 4(3): 1191-1222, 2022
C. Schenker, J. E. Cohen, and E. Acar. A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings, IEEE Journal of Selected Topics in Signal Processing, 15(3): 506-521, 2021
Join Zoom Meeting:
https://uio.zoom.us/j/63199595088?pwd=Y05GYkJwR1dCUHFhUnlGMFpsdGc3UT09
Meeting ID: 631 9959 5088
Passcode: 302551
Welcome!
Best regards,
Thea Roksvåg and Lars Henry Berge Olsen.
