Abstract
In many practical scenarios, complex high-dimensional data contains low-dimensional structures that could be informative of the analytic problems at hand. I will present a method that detects such structures if they exist, and uses them to construct compact interpretable models for different machine learning tasks that can benefit practical applications.
To start with, I will formalize Informative Projection Recovery, the problem of extracting a small set of low-dimensional projections of data that jointly support an accurate model for a given learning task. Our solution to this problem is a regression-based algorithm that identifies informative projections by optimizing over a matrix of point-wise loss estimators. It generalizes to multiple types of machine learning problems, offering solutions to classification, clustering, regression, and active learning tasks. Experiments show that our method can discover and leverage low-dimensional structures in data, yielding accurate and compact models. Our method is particularly useful in applications in which expert assessment of the results is of the essence, such as classification tasks in the healthcare domain.
Biography
Madalina Fiterau is a PhD student in Machine Learning at Carnegie Mellon University and a member of the Auton Lab. She is advised by Prof. Artur Dubrawski. Her research interests include query-specific models for decision support systems, learning with structured sparsity, dimensionality reduction in an active learning setting and anomalous pattern detection. She received her BE in Computer Engineering from the Politehnica University of Timisoara.