@inproceedings{fcf11b1d85ea43688c656fe0bfbc40d4,
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",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4239--4243",
booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",
}