@inproceedings{930681a486a54c359fe34931287fafe7,
title = "Collaborative representation based k-nearest neighbor classifier for hyperspectral imagery",
abstract = "We propose a novel collaborative representation based k-nearest neighbors algorithm for hyperspectral image classification. The proposed method is based on a collaborative representation computed by an ℓ2-norm minimization with a Tikhonov regularization matrix. More specific, the testing sample is represented as a linear combination of all the training samples, and the weights for representation are estimated by an ℓ2-norm minimization derived closed-form solution. The label of the testing sample is determined by the majority vote of those with k largest representation weights. The experimental results show that the proposed algorithm achieves better performance than several previous algorithms, such as the original k-nearest neighbor classifier and local mean based nearest neighbor classifier.",
keywords = "collaborative representation, hyperspectral data, nearest neighbors, pattern classification",
author = "Wei Li and Qian Du and Fan Zhang and Wei Hu",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 ; Conference date: 24-06-2014 Through 27-06-2014",
year = "2014",
month = jun,
day = "28",
doi = "10.1109/WHISPERS.2014.8077601",
language = "English",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2014 6th Workshop on Hyperspectral Image and Signal Processing",
address = "United States",
}