Orthogonal Tensor Decompositions
SIAM Journal on Matrix Analysis and Applications
Higher-Order Web Link Analysis Using Multilinear Algebra
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Computing non-negative tensor factorizations
Optimization Methods & Software - Mathematical programming in data mining and machine learning
Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem
SIAM Journal on Matrix Analysis and Applications
Tensor Decompositions and Applications
SIAM Review
Practical Approaches to Principal Component Analysis in the Presence of Missing Values
The Journal of Machine Learning Research
| Hi-index | 0.00 |
We propose a probabilistic model class for the analysis of three-way count data, motivated by studying the subjectivity of language. Our models are applicable for instance to a data tensor of how many times each subject used each term in each context, thus revealing individual variation in natural language use. As our main goal is exploratory analysis, we propose hybrid bilinear and trilinear models with zero-mean constraints, separating modeling the simpler and more complex phenomena. While helping exploratory analysis, this approach leads into a more involved model selection problem. Our solution by forward selection guided by cross-validation likelihood is shown to work reliably on experiments with synthetic data.