Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Manifold alignment using Procrustes analysis
Proceedings of the 25th international conference on Machine learning
Pseudo-aligned multilingual corpora
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multiscale analysis of document corpora based on diffusion models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Matching samples of multiple views
Data Mining and Knowledge Discovery
Efficiency investigation of manifold matching for text document classification
Pattern Recognition Letters
Neighborhood Correlation Analysis for Semi-paired Two-View Data
Neural Processing Letters
Manifold alignment preserving global geometry
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Manifold alignment based on sparse local structures of more corresponding pairs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Manifold alignment has been found to be useful in many areas of machine learning and data mining. In this paper we introduce a novel manifold alignment approach, which differs from "semi-supervised alignment" and "Procrustes alignment" in that it does not require predetermining correspondences. Our approach learns a projection that maps data instances (from two different spaces) to a lower dimensional space simultaneously matching the local geometry and preserving the neighborhood relationship within each set. This approach also builds connections between spaces defined by different features and makes direct knowledge transfer possible. The performance of our algorithm is demonstrated and validated in a series of carefully designed experiments in information retrieval and bioinformatics.