Comanifold learning with missing data
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Proceedings of the 36th International Conference on Machine Learning, PMLR 97:46054614, 2019.
Abstract
Representation learning is typically applied to only one mode of a data matrix, either its rows or columns. Yet in many applications, there is an underlying geometry to both the rows and the columns. We propose utilizing this coupled structure to perform comanifold learning: uncovering the underlying geometry of both the rows and the columns of a given matrix, where we focus on a missing data setting. Our unsupervised approach consists of three components. We first solve a family of optimization problems to estimate a complete matrix at multiple scales of smoothness. We then use this collection of smooth matrix estimates to compute pairwise distances on the rows and columns based on a new multiscale metric that implicitly introduces a coupling between the rows and the columns. Finally, we construct row and column representations from these multiscale metrics. We demonstrate that our approach outperforms competing methods in both data visualization and clustering.
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