Proceedings Article10.1109/IJCNN.2015.7280313
Learning discriminant isomap for dimensionality reduction
Bo Yang,Ming Xiang,Yupei Zhang +2 more
- 12 Jul 2015
- pp 1-8
3
TL;DR: 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 betweenData points of different classes.
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Abstract: 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.
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Citations
A Manifold Learning Two-Tier Beamforming Scheme Optimizes Resource Management in Massive MIMO Networks
TL;DR: A manifold learning two-tier beamforming (MLTB) scheme is proposed to enable efficient and low-complexity operation in large scale dimensional MIMO systems and can obtain near-optimal sum-rate and considerably higher energy efficiency than the conventional schemes.
New supervised locally linear embedding for dimensionality reduction using distance metric learning
TL;DR: A new supervised dimensionality reduction method based on Locally Linear Embedding and Distance Metric Learning is proposed based on a linear discriminant transformation learnt from distance metric learning to reduce the dimensionality of data points.
Multi-manifold Discriminant Isomap for visualization and classification
Bo Yang,Ming Xiang,Yupei Zhang +2 more
TL;DR: In both visualization and classification experiments, MMD-Isomap achieves improved performance over many state-of-the-art methods and two new numerical metrics are designed to measure the performance of dimensionality-reduction method.
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