ECE/Hariri Institute Distinguished Seminar: Bruno Sinopoli

ECE/Hariri Institute Seminar: Bruno Sinopoli

Title: Linear Methods for Dimensionality Reduction: is there life beyond PCA?

Abstract: Feature extraction and selection at the presence of nonlinear dependencies among the data is a fundamental challenge in unsupervised learning. We propose using a Gram-Schmidt (GS) type orthogonalization process over function spaces to detect and map out such dependencies. Specifically, by applying the GS process over some family of functions, we construct a series of covariance matrices that can either be used to identify new large-variance directions, or to remove those dependencies from known directions. In the former case, we provide information-theoretic guarantees in terms of entropy reduction. In the latter, we provide precise conditions by which the chosen function family eliminates existing redundancy in the data. Each approach provides both a feature extraction and a feature selection algorithm. Our feature extraction methods are linear, and can be seen as natural generalization of principal component analysis (PCA). We provide experimental results for synthetic and real-world benchmark datasets which show superior performance over state-of-the-art (linear) feature extraction and selection algorithms. Surprisingly, our linear feature extraction algorithms are comparable and often outperform several important nonlinear feature extraction methods such as autoencoders, kernel PCA, and UMAP.

Bio: Bruno Sinopoli is the Truet B. Thompson Professor and the Director of School of Electrical, Computer and Energy Engineering at Arizona State University. Prior to that, he was the Das Family Distinguished Professor and Chair of the Preston M. Green Department of Electrical & Systems Engineering at the McKelvey School of Engineering at Washington University in St Louis, from 2019 to 2025. Prior to joining Washington University, Prof. Sinopoli was a professor in the Electrical and Computer Engineering Department at Carnegie Mellon University from 2007 to 2019, with courtesy appointments in the Robotics Institute and the Mechanical Engineering Department and co-director of the Smart Infrastructure Institute. Previously, he was a postdoctoral fellow at the University of California, Berkeley, and Stanford University from 2005 to 2007. He received his M.S. and Ph.D. in Electrical Engineering at the University of California at Berkeley in 2003 and 2005, respectively, and his Laurea from the University of Padova in Italy. His research focuses on robust and resilient design of cyber-physical systems, with applications to smart infrastructures, networked and distributed multi-agent control systems, cloud computing, and energy systems.