Abundant inverse regression using sufficient reduction and its applications

Hyun Woo Kim, Brandon M. Smith, Nagesh Adluru, Charles R. Dyer, Sterling C. Johnson, Vikas Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Statistical models such as linear regression drive numerous applications in computer vision and machine learning. The landscape of practical deployments of these formulations is dominated by forward regression models that estimate the parameters of a function mapping a set of p covariates, x, to a response variable, y. The less known alternative, Inverse Regression, offers various benefits that are much less explored in vision problems. The goal of this paper is to show how Inverse Regression in the “abundant” feature setting (i.e., many subsets of features are associated with the target label or response, as is the case for images), together with a statistical construction called Sufficient Reduction, yields highly flexible models that are a natural fit for model estimation tasks in vision. Specifically, we obtain formulations that provide relevance of individual covariates used in prediction, at the level of specific examples/samples — in a sense, explaining why a particular prediction was made. With no compromise in performance relative to other methods, an ability to interpret why a learning algorithm is behaving in a specific way for each prediction, adds significant value in numerous applications. We illustrate these properties and the benefits of Abundant Inverse Regression on three distinct applications.

Original languageEnglish
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsJiri Matas, Nicu Sebe, Max Welling, Bastian Leibe
PublisherSpringer Verlag
Pages570-584
Number of pages15
ISBN (Print)9783319464862
DOIs
Publication statusPublished - 2016 Jan 1
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9907 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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Keywords

  • Abundant regression
  • Age estimation
  • Alzheimer’s disease
  • Inverse regression
  • Kernel regression
  • Temperature prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kim, H. W., Smith, B. M., Adluru, N., Dyer, C. R., Johnson, S. C., & Singh, V. (2016). Abundant inverse regression using sufficient reduction and its applications. In J. Matas, N. Sebe, M. Welling, & B. Leibe (Eds.), Computer Vision - 14th European Conference, ECCV 2016, Proceedings (pp. 570-584). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9907 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46487-9_35