@inproceedings{143aad3efb37456a9248f840ac64d7b0,
title = "Subspace learning via low rank projections for dimensionality reduction",
abstract = "Subspace learning algorithms aim at finding low dimensional linear manifolds that are representative of the data at hand. In this paper we propose a semi-supervised approach that fits any given dataset to a low dimensional subspace while maintaining class separability. Our approach has no tunable parameters as against many existing subspace learning algorithms which obviates the need for cross-validation. We apply our algorithm to the problem of face recognition. We perform both qualitative as well as quantitative experiments on multiple real world datasets. For qualitative analysis we visualize the class separability of binary and multi-class projected data. For quantitative analysis, we perform classification experiments on projected data and achieve state-of-the-art results compared to popular existing dimensionality reduction methods.",
author = "Devansh Arpit and Chetan Ramaiah and Venu Govindaraju",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 8th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2016 ; Conference date: 06-09-2016 Through 09-09-2016",
year = "2016",
month = dec,
day = "19",
doi = "10.1109/BTAS.2016.7791185",
language = "English",
series = "2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems, BTAS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems, BTAS 2016",
address = "United States",
}