Manifold-Based Inference for a Supervised Gaussian Process Classifier

Anis Fradi; Chafik Samir; Anne Franccoise Yao

doi:10.1109/ICASSP.2018.8461840

Manifold-Based Inference for a Supervised Gaussian Process Classifier

Anis Fradi
, Chafik Samir
, Anne Franccoise Yao

Clermont-Auvergne University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

One of the challenging classification problems consists of learning relevant and meaningful relationships between high dimensional representations across a relatively few observed individuals. Since this problem could have drastic effects on the classification performance, we propose a Bayesian alternative in the case of logistic regression. The proposed method has the additional benefit to learn both the adaptive embedding, as a Gaussian process, and the dimensionality reduction, jointly within the same Bayesian framework. We illustrate the efficiency and the accuracy of our framework for classifying images of manufacturing defects.

Original language	English
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4239-4243
Number of pages	5
ISBN (Print)	9781538646588
DOIs	https://doi.org/10.1109/ICASSP.2018.8461840
Publication status	Published - 10 Sept 2018
Externally published	Yes
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2018-April
ISSN (Print)	1520-6149

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	15/04/18 → 20/04/18

Keywords

Gaussian Process
Image Classification
Machine Learning
Manifold Embedding
Regression

Access to Document

10.1109/ICASSP.2018.8461840

Cite this

Fradi, A., Samir, C., & Yao, A. F. (2018). Manifold-Based Inference for a Supervised Gaussian Process Classifier. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 4239-4243). Article 8461840 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8461840

Fradi, Anis ; Samir, Chafik ; Yao, Anne Franccoise. / Manifold-Based Inference for a Supervised Gaussian Process Classifier. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 4239-4243 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "Manifold-Based Inference for a Supervised Gaussian Process Classifier",

abstract = "One of the challenging classification problems consists of learning relevant and meaningful relationships between high dimensional representations across a relatively few observed individuals. Since this problem could have drastic effects on the classification performance, we propose a Bayesian alternative in the case of logistic regression. The proposed method has the additional benefit to learn both the adaptive embedding, as a Gaussian process, and the dimensionality reduction, jointly within the same Bayesian framework. We illustrate the efficiency and the accuracy of our framework for classifying images of manufacturing defects.",

keywords = "Gaussian Process, Image Classification, Machine Learning, Manifold Embedding, Regression",

author = "Anis Fradi and Chafik Samir and Yao, \{Anne Franccoise\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",

year = "2018",

month = sep,

day = "10",

doi = "10.1109/ICASSP.2018.8461840",

language = "English",

isbn = "9781538646588",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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Fradi, A, Samir, C & Yao, AF 2018, Manifold-Based Inference for a Supervised Gaussian Process Classifier. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8461840, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018-April, Institute of Electrical and Electronics Engineers Inc., pp. 4239-4243, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 15/04/18. https://doi.org/10.1109/ICASSP.2018.8461840

Manifold-Based Inference for a Supervised Gaussian Process Classifier. / Fradi, Anis; Samir, Chafik; Yao, Anne Franccoise.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 4239-4243 8461840 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - One of the challenging classification problems consists of learning relevant and meaningful relationships between high dimensional representations across a relatively few observed individuals. Since this problem could have drastic effects on the classification performance, we propose a Bayesian alternative in the case of logistic regression. The proposed method has the additional benefit to learn both the adaptive embedding, as a Gaussian process, and the dimensionality reduction, jointly within the same Bayesian framework. We illustrate the efficiency and the accuracy of our framework for classifying images of manufacturing defects.

AB - One of the challenging classification problems consists of learning relevant and meaningful relationships between high dimensional representations across a relatively few observed individuals. Since this problem could have drastic effects on the classification performance, we propose a Bayesian alternative in the case of logistic regression. The proposed method has the additional benefit to learn both the adaptive embedding, as a Gaussian process, and the dimensionality reduction, jointly within the same Bayesian framework. We illustrate the efficiency and the accuracy of our framework for classifying images of manufacturing defects.

KW - Gaussian Process

KW - Image Classification

KW - Machine Learning

KW - Manifold Embedding

KW - Regression

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U2 - 10.1109/ICASSP.2018.8461840

DO - 10.1109/ICASSP.2018.8461840

M3 - Conference contribution

AN - SCOPUS:85054244062

SN - 9781538646588

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

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BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 15 April 2018 through 20 April 2018

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Fradi A, Samir C, Yao AF. Manifold-Based Inference for a Supervised Gaussian Process Classifier. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 4239-4243. 8461840. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2018.8461840

Manifold-Based Inference for a Supervised Gaussian Process Classifier

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