Semi-supervised local fisher discriminant analysis for dimensionality reduction

Authors:
Masashi Sugiyama;Tsuyoshi Idé;Shinichi Nakajima;Jun Sese
Affiliations:
Tokyo Institute of Technology, Tokyo, Japan;IBM Research, Kanagawa, Japan;Nikon Corporation, Saitama, Japan;Ochanomizu University, Tokyo, Japan
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

Citing 5
Cited 6

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Soft Margins for AdaBoost

Machine Learning
Kernels for Semi-Structured Data

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis

The Journal of Machine Learning Research
Covariate Shift Adaptation by Importance Weighted Cross Validation

The Journal of Machine Learning Research

Semi-supervised orthogonal discriminant analysis via label propagation

Pattern Recognition
A unified semi-supervised dimensionality reduction framework for manifold learning

Neurocomputing
Semi-supervised graph embedding scheme with active learning (SSGEAL): classifying high dimensional biomedical data

PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Dimensionality reduction via compressive sensing

Pattern Recognition Letters
Self-adaptive local Fisher discriminant analysis for semi-supervised image recognition

International Journal of Biometrics
A priori-driven PCA

ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled samples could be useful in improving the performance. In this paper, we propose a semi-supervised dimensionality reduction method which preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The proposed method has an analytic form of the globally optimal solution and it can be computed based on eigendecompositions. Therefore, the proposed method is computationally reliable and efficient. We show the effectiveness of the proposed method through extensive simulations with benchmark data sets.