Tensor Decompositions for Learning Latent Variable Models
Authors: Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a convergence analysis of this method for orthogonally decomposable symmetric tensors, as well as a detailed perturbation analysis for a robust (and a computationally tractable) variant (Theorem 5.1). This perturbation analysis can be viewed as an analogue of Wedin s perturbation theorem for singular vectors of matrices (Wedin, 1972) |
| Researcher Affiliation | Collaboration | Animashree Anandkumar EMAIL Electrical Engineering and Computer Science University of California, Irvine... Rong Ge EMAIL Microsoft Research... |
| Pseudocode | Yes | Algorithm 1 Robust tensor power method input symmetric tensor T Rk k k, number of iterations L, N. output the estimated eigenvector/eigenvalue pair; the deflated tensor. |
| Open Source Code | No | The paper does not explicitly state that code is made available, nor does it provide any links to source code repositories. |
| Open Datasets | No | The paper discusses various latent variable models and their theoretical properties but does not describe experiments performed on specific datasets or provide access information for any datasets. |
| Dataset Splits | No | The paper focuses on theoretical analysis and algorithm design for latent variable models, and thus does not include details on dataset splits for experimental reproduction. |
| Hardware Specification | No | The paper focuses on theoretical computational complexity and algorithm analysis, and therefore does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe implementation details or specific software dependencies with version numbers used in the authors' work. |
| Experiment Setup | No | The paper presents theoretical methods and analyses without conducting empirical experiments, and thus does not include details on experimental setup or hyperparameter configurations. |