Tensor denoising and completion based on ordinal observations
| Autor: | Lee, Chanwoo, Wang, Miaoyan |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Proceedings of the 37th International Conference on Machine Learning(ICML), 2020 |
| Druh dokumentu: | Working Paper |
| Popis: | Higher-order tensors arise frequently in applications such as neuroimaging, recommendation system, social network analysis, and psychological studies. We consider the problem of low-rank tensor estimation from possibly incomplete, ordinal-valued observations. Two related problems are studied, one on tensor denoising and the other on tensor completion. We propose a multi-linear cumulative link model, develop a rank-constrained M-estimator, and obtain theoretical accuracy guarantees. Our mean squared error bound enjoys a faster convergence rate than previous results, and we show that the proposed estimator is minimax optimal under the class of low-rank models. Furthermore, the procedure developed serves as an efficient completion method which guarantees consistent recovery of an order-$K$ $(d,\ldots,d)$-dimensional low-rank tensor using only $\tilde{\mathcal{O}}(Kd)$ noisy, quantized observations. We demonstrate the outperformance of our approach over previous methods on the tasks of clustering and collaborative filtering. Comment: 35 pages, 6 figures |
| Databáze: | arXiv |
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