Large margin nearest local mean classifier

Authors:
Jing Chai;Hongwei Liu;Bo Chen;Zheng Bao
Affiliations:
National Laboratory of Radar Signal Processing, Xidian University, Xi'an, Shanxi, China;National Laboratory of Radar Signal Processing, Xidian University, Xi'an, Shanxi, China;Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA;National Laboratory of Radar Signal Processing, Xidian University, Xi'an, Shanxi, China
Venue:
Signal Processing
Year:
2010

Citing 14
Cited 3

Semidefinite programming

SIAM Review
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Convex Optimization

Convex Optimization
Margin based feature selection - theory and algorithms

ICML '04 Proceedings of the twenty-first international conference on Machine learning
LESS: A Model-Based Classifier for Sparse Subspaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
A non-probabilistic recognizer of stochastic signals based on KLT

Signal Processing
An efficient algorithm for local distance metric learning

AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The Laplacian Classifier

IEEE Transactions on Signal Processing - Part I
A two-distribution compounded statistical model for Radar HRRP target recognition

IEEE Transactions on Signal Processing - Part I
Radar HRRP target recognition based on higher order spectra

IEEE Transactions on Signal Processing
Discriminative components of data

IEEE Transactions on Neural Networks

Time series classification by class-specific Mahalanobis distance measures

Advances in Data Analysis and Classification
Metric learning for large scale image classification: generalizing to new classes at near-zero cost

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Multi-view action recognition based on action volumes, fuzzy distances and cluster discriminant analysis

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

Distance metric learning and classifier design are two highly challenging tasks in the machine learning community. In this paper we propose a new large margin nearest local mean (LMNLM) scheme to consider them jointly, which aims at improving the separability between local parts of different classes. We adopt 'local mean vector' as the basic classification model, and then through linear transformation, large margins between heterogeneous local parts are introduced. Moreover, by eigenvalue decomposition, we may also reduce data's dimensions. LMNLM can be formulated as a semidefinite programming (SDP) problem, so it is assured to converge globally. Experimental results show that LMNLM is a promising algorithm due to its leading to high classification accuracies and low dimensions.