Title: Expectation Distance-based Distributional Clustering for Noise-Robustness
Abstract: The classical clustering algorithms work with raw-data and are not designed to be robust to uncertain/noisy data. However, data is naturally and inherently affected by the random nature of the physical generation process and measurement inaccuracies, sampling discrepancy, outdated data sources, or other errors, making it prone to noise/uncertainty. This work presents a clustering technique that reduces the susceptibility to data noise by learning and clustering the data-distribution and then assigning the data to the cluster of its distribution. In the process, it reduces the impact of noise on clustering results. This method involves introducing a new distance among distributions, namely the expectation distance, that goes beyond the state-of-art distribution distance of optimal mass transport, also called 2-Wasserstein: the latter essentially depends only on the marginal distributions while the former also employs the information about the joint distributions, making it more powerful. Using the ED, the work extends the classical K-means and K-medoids clustering to those over data-distributions (rather than raw-data) and further introduces K-medoids using the 2-Wasserstein distance.
Bio: Rahmat Kemisola Adesunkanmi is a Ph.D. candidate at the Department of Electrical and Computer Engineering, Iowa State University, Ames, USA. She received her B.S. degree from the University of Ibadan, Nigeria, in 2015 and, subsequently, her master’s degree from Iowa State University in 2021. Her research interests include control, data-driven analysis, robust machine learning techniques, and time series analysis. She is an active member of the Graduate Society for Women Engineers.
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