TY - GEN
T1 - A novel semi-supervised feature extraction algorithm
AU - He, Mingyi
AU - Qu, Xiaogang
AU - Mei, Shaohui
PY - 2011
Y1 - 2011
N2 - Supervised feature extraction algorithms usually require lots of labeled samples to achieve good performance. However, labeling the samples is often time-consuming and even impractical. Therefore, in this paper, a semi-supervised manifold local Fisher discriminant analysis (SMLFDA) is proposed to take advantage of unlabeled samples as well as labeled samples. The proposed algorithm utilizes local scatter matrix and manifold structure to extract the information from labeled and unlabeled samples, respectively, which significantly improves the accuracy of successive classification application when labeled samples are insufficient. In addition, an exponential form weighting coefficient is proposed to further improve the classification performance. Experiments of hyperspectral classification demonstrate the effectiveness of the proposed semi-supervised feature extraction algorithm.
AB - Supervised feature extraction algorithms usually require lots of labeled samples to achieve good performance. However, labeling the samples is often time-consuming and even impractical. Therefore, in this paper, a semi-supervised manifold local Fisher discriminant analysis (SMLFDA) is proposed to take advantage of unlabeled samples as well as labeled samples. The proposed algorithm utilizes local scatter matrix and manifold structure to extract the information from labeled and unlabeled samples, respectively, which significantly improves the accuracy of successive classification application when labeled samples are insufficient. In addition, an exponential form weighting coefficient is proposed to further improve the classification performance. Experiments of hyperspectral classification demonstrate the effectiveness of the proposed semi-supervised feature extraction algorithm.
KW - classification
KW - hyperspectral data
KW - local Fisher discriminant analysis
KW - manifold learning
KW - semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=80052193993&partnerID=8YFLogxK
U2 - 10.1109/ICIEA.2011.5975623
DO - 10.1109/ICIEA.2011.5975623
M3 - 会议稿件
AN - SCOPUS:80052193993
SN - 9781424487554
T3 - Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011
SP - 436
EP - 440
BT - Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011
T2 - 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011
Y2 - 21 June 2011 through 23 June 2011
ER -