TY - GEN
T1 - Learning discriminant isomap for dimensionality reduction
AU - Yang, Bo
AU - Xiang, Ming
AU - Zhang, Yupei
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/28
Y1 - 2015/9/28
N2 - In order to extend the unsupervised nonlinear dimensionality reduction method Isomap for use in supervised learning, a new supervised manifold learning method namely discriminant Isomap (D-Isomap) is proposed, in which the geometrical structure of each class data is preserved by keeping geodesic distances between data points of the same class and the discriminant capacity is enhanced by maximizing the distances between data points of different classes. A new objective function is defined for this purpose and the corresponding optimization problem is solved by using the SMACOF algorithm. The effectiveness of D-Isomap is examined by extensive simulations on artificial and real-world data sets, including MNIST, USPS, and UCI. In both visualization and classification experiments, D-Isomap achieves comparable or better performance than the widely used dimensionality reduction algorithms.
AB - In order to extend the unsupervised nonlinear dimensionality reduction method Isomap for use in supervised learning, a new supervised manifold learning method namely discriminant Isomap (D-Isomap) is proposed, in which the geometrical structure of each class data is preserved by keeping geodesic distances between data points of the same class and the discriminant capacity is enhanced by maximizing the distances between data points of different classes. A new objective function is defined for this purpose and the corresponding optimization problem is solved by using the SMACOF algorithm. The effectiveness of D-Isomap is examined by extensive simulations on artificial and real-world data sets, including MNIST, USPS, and UCI. In both visualization and classification experiments, D-Isomap achieves comparable or better performance than the widely used dimensionality reduction algorithms.
KW - Electronics packaging
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=84951152266&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2015.7280313
DO - 10.1109/IJCNN.2015.7280313
M3 - 会议稿件
AN - SCOPUS:84951152266
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2015 International Joint Conference on Neural Networks, IJCNN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Joint Conference on Neural Networks, IJCNN 2015
Y2 - 12 July 2015 through 17 July 2015
ER -