@inproceedings{7790347a7aad4967a1ce97fda23a2a90,
title = "Alignment with intra-class structure can improve classification",
abstract = "High dimensional data is modeled using low-rank subspaces, and the probability of misclassification is expressed in terms of the principal angles between subspaces. The form taken by this expression motivates the design of a new feature extraction method that enlarges inter-class separation, while preserving intra-class structure. The method can be tuned to emphasize different features shared by members within the same class. Classification performance is compared to that of state-of-the-art methods on synthetic data and on the real face database. The probability of misclassification is decreased when intra-class structure is taken into account.",
keywords = "classification, feature extraction, principal angle, subspace",
author = "Jiaji Huang and Qiang Qiu and Robert Calderbank and Miguel Rodrigues and Guillermo Sapiro",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 ; Conference date: 19-04-2014 Through 24-04-2014",
year = "2015",
month = aug,
day = "4",
doi = "10.1109/ICASSP.2015.7178305",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1921--1925",
booktitle = "2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings",
address = "United States",
}