A Modified Semi-Supervised Learning Algorithm on Laplacian Eigenmaps

Authors:
Zhong-Qiu Zhao;Jun-Zhao Li;Jun Gao;Xindong Wu
Affiliations:
College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China 230009;College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China 230009;College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China 230009;College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China 230009 and Department of Computer Science, University of Vermont, Burlington, USA
Venue:
Neural Processing Letters
Year:
2010

Citing 6
Cited 2

Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Semi-Supervised Learning on Riemannian Manifolds

Machine Learning
Semi-supervised learning with an imperfect supervisor

Knowledge and Information Systems
Embedding new data points for manifold learning via coordinate propagation

Knowledge and Information Systems
Semi-supervised learning by disagreement

Knowledge and Information Systems
Dimensionality reduction for semi-supervised face recognition

FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II

Multiple-View Multiple-Learner Semi-Supervised Learning

Neural Processing Letters
A comparative study of nonlinear manifold learning methods for cancer microarray data classification

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this letter, a modified algorithm is proposed to extend 2-class semi-supervised learning on Laplacian eigenmaps to multi-class learning problems. The modified algorithm significantly increases its learning speed, and at the same time attains a satisfactory classification performance that is not lower than the original algorithm.