TY - GEN
T1 - Joint dimensionality reduction and metric learning
T2 - 34th International Conference on Machine Learning, ICML 2017
AU - Harandi, Mehrtash
AU - Salzmann, Mathieu
AU - Hartley, Richard
N1 - Publisher Copyright:
Copyright 2017 by the author(s).
PY - 2017
Y1 - 2017
N2 - To be tractable and robust to data noise, existing metric learning algorithms commonly rely on PCA as a pre-processing step. How can we know, however, that PCA, or any other specific dimensionality reduction technique, is the method of choice for the problem at hand? The answer is simple: We cannot! To address this issue, in this paper, we develop a Riemannian framework to jointly learn a mapping performing dimensionality reduction and a metric in the induced space. Our experiments evidence that, while wc directly work on high-dimensional features, our approach yields competitive runtimes with and higher accuracy than state-of-the-art metric learning algorithms.
AB - To be tractable and robust to data noise, existing metric learning algorithms commonly rely on PCA as a pre-processing step. How can we know, however, that PCA, or any other specific dimensionality reduction technique, is the method of choice for the problem at hand? The answer is simple: We cannot! To address this issue, in this paper, we develop a Riemannian framework to jointly learn a mapping performing dimensionality reduction and a metric in the induced space. Our experiments evidence that, while wc directly work on high-dimensional features, our approach yields competitive runtimes with and higher accuracy than state-of-the-art metric learning algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85048260955&partnerID=8YFLogxK
M3 - Conference contribution
T3 - 34th International Conference on Machine Learning, ICML 2017
SP - 2244
EP - 2256
BT - 34th International Conference on Machine Learning, ICML 2017
PB - International Machine Learning Society (IMLS)
Y2 - 6 August 2017 through 11 August 2017
ER -