Name
Abstract | ||
|---|---|---|
Low-rank tensor learning has many applications in machine learning. A series of batch learning algorithms have achieved great successes. However, in many emerging applications, such as climate data analysis, we are confronted with large-scale tensor streams, which poses significant challenges to existing solution in terms of computational costs and limited response time. In this paper, we propose an online accelerated low-rank tensor learning algorithm (ALTO) to solve the problem. At each iteration, we project the current tensor to the subspace of low-rank tensors in order to perform efficient tensor decomposition, then recover the decomposition of the new tensor. By randomly glancing at additional subspaces, we successfully avoid local optima at negligible extra computational cost. We evaluate our method on two tasks in streaming multivariate spatio-temporal analysis: online forecasting and multi-model ensemble, which shows that our method achieves comparable predictive accuracy with significant boost in run time. |
Year | Venue | Field |
|---|---|---|
2015 | International Conference on Machine Learning | Tensor,Multivariate statistics,Computer science,Local optimum,Linear subspace,Artificial intelligence,Multilinear subspace learning,Machine learning,Tensor decomposition |
DocType | Citations | PageRank |
Conference | 17 | 0.62 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Yu | 1 | 188 | 12.87 |
| Dehua Cheng | 2 | 54 | 6.46 |
| Yan Liu | 3 | 2551 | 189.16 |