Letters: Extended local tangent space alignment for classification

Authors:
Jing Wang;Wenxian Jiang;Jin Gou
Affiliations:
The School of Computer Science and Technology, Huaqiao University, Xiamen City 361021, PR China;The School of Computer Science and Technology, Huaqiao University, Xiamen City 361021, PR China;The School of Computer Science and Technology, Huaqiao University, Xiamen City 361021, PR China
Venue:
Neurocomputing
Year:
2012

Citing 15
Cited 1

Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database for Handwritten Text Recognition Research

IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-linear dimensionality reduction techniques for classification and visualization

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds

The Journal of Machine Learning Research
Semi-Supervised Learning on Riemannian Manifolds

Machine Learning
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

SIAM Journal on Scientific Computing
Semi-supervised nonlinear dimensionality reduction

ICML '06 Proceedings of the 23rd international conference on Machine learning
Robust locally linear embedding

Pattern Recognition
Improve local tangent space alignment using various dimensional local coordinates

Neurocomputing
Weighted locally linear embedding for dimension reduction

Pattern Recognition
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Nonlinear embedding preserving multiple local-linearities

Pattern Recognition
Nearest manifold approach for face recognition

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Robust local tangent space alignment via iterative weighted PCA

Neurocomputing

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

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

The local tangent space alignment (LTSA) has demonstrated promising results in finding meaningful low-dimensional structures hidden in high-dimensional data. However, LTSA may have a limited effectiveness on the data which are organized in multiple classes or contain noisy points. In this paper, the distances between the samples and their neighbors are rescaled by using the reconstruction weights to overcome the limitation. An extension of LTSA is proposed based on the local rescaled distance matrix. Numerical experiments on both synthetic and real-world data sets are used to show the improvement of our extension for classification and the robustness to noisy data.