Multiple view semi-supervised dimensionality reduction

Authors:
Chenping Hou;Changshui Zhang;Yi Wu;Feiping Nie
Affiliations:
Department of Mathematics and Systems Science, National University of Defense Technology, Changsha 410073, China and State Key Laboratory of Intelligent Technology and Systems, Tsinghua National L ...;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100 ...;Department of Mathematics and Systems Science, National University of Defense Technology, Changsha 410073, China;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100 ...
Venue:
Pattern Recognition
Year:
2010

Citing 11
Cited 8

Combinatorial optimization: algorithms and complexity

Combinatorial optimization: algorithms and complexity
Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA versus LDA

IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions

The Journal of Machine Learning Research
Think globally, fit locally: unsupervised learning of low dimensional manifolds

The Journal of Machine Learning Research
Meta-clustering of gene expression data and literature-based information

ACM SIGKDD Explorations Newsletter
Multi-View Clustering

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Semi-supervised nonlinear dimensionality reduction

ICML '06 Proceedings of the 23rd international conference on Machine learning
Spectral clustering and transductive learning with multiple views

Proceedings of the 24th international conference on Machine learning
Multi-view local learning

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2

Semi-supervised Elastic net for pedestrian counting

Pattern Recognition
Feature Fusion Using Multiple Component Analysis

Neural Processing Letters
A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data

Pattern Recognition
Multiple outlooks learning with support vector machines

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Rank canonical correlation analysis and its application in visual search reranking

Signal Processing
Statistical shape model for manifold regularization: Gleason grading of prostate histology

Computer Vision and Image Understanding
Neighborhood Correlation Analysis for Semi-paired Two-View Data

Neural Processing Letters
Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

Multiple view data, together with some domain knowledge in the form of pairwise constraints, arise in various data mining applications. How to learn a hidden consensus pattern in the low dimensional space is a challenging problem. In this paper, we propose a new method for multiple view semi-supervised dimensionality reduction. The pairwise constraints are used to derive embedding in each view and simultaneously, the linear transformation is introduced to make different embeddings from different pattern spaces comparable. Hence, the consensus pattern can be learned from multiple embeddings of multiple representations. We derive an iterating algorithm to solve the above problem. Some theoretical analyses and out-of-sample extensions are also provided. Promising experiments on various data sets, together with some important discussions, are also presented to demonstrate the effectiveness of the proposed algorithm.