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Greedy Gaussian Process Regression Applied to Object Categorization and Regression

Published: 03 May 2020 Publication History

Abstract

In this work we propose an approximation of Gaussian Process and apply it to Classification and Regression tasks. We, primarily, target the problem of visual object categorization using a Greedy variant of Gaussian Processes. To deal with the prohibitive training and inferencing cost of GP, we devise a greedy approach to subset selection and the inducing input choice to approximate the kernel matrix, resulting in faster retrieval timings. A localized combination of kernel functions is designed and used in a framework of sparse approximations to Gaussian Processes for visual object categorization and generic regression tasks. Through exhaustive experimentation and empirical results we demonstrate the effectiveness of the proposed approach, when compared with other kernel based methods.

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  • (2022)Fixed-budget approximation of the inverse kernel matrix for identification of nonlinear dynamic processesJournal of Applied Engineering Science10.5937/jaes0-3177220:1(150-159)Online publication date: 2022

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  1. Greedy Gaussian Process Regression Applied to Object Categorization and Regression

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      cover image ACM Other conferences
      ICVGIP '18: Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing
      December 2018
      659 pages
      ISBN:9781450366151
      DOI:10.1145/3293353
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 03 May 2020

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      Author Tags

      1. Gaussian Process
      2. Object Detection
      3. Regression
      4. Sparse Approximation

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      • (2022)Fixed-budget approximation of the inverse kernel matrix for identification of nonlinear dynamic processesJournal of Applied Engineering Science10.5937/jaes0-3177220:1(150-159)Online publication date: 2022

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