Informative Projection Recovery for Classification, Clustering and Regression

Abstract

Data driven decision support systems often benefit from human participation to validate outcomes produced by automated procedures. Perceived utility hinges on the system's ability to learn transparent, comprehensible models from data. We introduce and formalize Informative Projection Recovery: the problem of extracting a set of low-dimensional projections of data which jointly form an accurate solution to a given learning task. We approach this problem with RIPR: a regression-based algorithm that identifies informative projections by optimizing over a matrix of point-wise loss estimators. It generalizes from our previous algorithm, offering solutions to classification, clustering, and regression tasks. Experiments show that RIPR can discover and leverage structures of informative projections in data, if they exist, while yielding accurate and compact models. It is particularly useful in applications involving multivariate numeric data in which expert assessment of the results is of the essence.