Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
The Journal of Machine Learning Research
Computational Linguistics - Special issue on web as corpus
A program for aligning sentences in bilingual corpora
Computational Linguistics - Special issue on using large corpora: I
Data Fusion and Multicue Data Matching by Diffusion Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold alignment using Procrustes analysis
Proceedings of the 25th international conference on Machine learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Pseudo-aligned multilingual corpora
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Manifold alignment without correspondence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IEEE Transactions on Knowledge and Data Engineering
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This paper proposes a novel algorithm for manifold alignment preserving global geometry. This approach constructs mapping functions that project data instances from different input domains to a new lower-dimensional space, simultaneously matching the instances in correspondence and preserving global distances between instances within the original domains. In contrast to previous approaches, which are largely based on preserving local geometry, the proposed approach is suited to applications where the global manifold geometry needs to be respected. We evaluate the effectiveness of our algorithm for transfer learning in two real-world cross-lingual information retrieval tasks.