Outlier-Robust Multi-Aspect Streaming Tensor Completion and Factorization
Authors: Mehrnaz Najafi, Lifang He, Philip S. Yu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on various real-world datasets show the superiority of the proposed method over the baselines and its robustness against outliers. |
| Researcher Affiliation | Academia | Mehrnaz Najafi1 , Lifang He2 , Philip S. Yu1 1Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA 2Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA {mnajaf2, psyu}@uic.edu, lifanghescut@gmail.com |
| Pseudocode | Yes | Algorithm 1 Outlier-Robust Streaming Tensor Completion and Factorization (OR-MSTC) |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | The datasets used in the paper are Cardiac MRI (CMRI) [Sharif and Bresler, 2007], downsampled Yelp [Jeon et al., 2016] and Network Traffic (NT) [Lakhina et al., 2004], and summarized in Table 3. |
| Dataset Splits | No | The paper mentions 'we randomly cover a percentage of data (missing percentage) ({10%, 20%})' and 'consider the remaining entries as observed information' but does not explicitly provide details about training, validation, and test splits for the observed data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various algorithms and methods (e.g., CP-ALS, ADMM, NNCP) but does not provide specific software dependencies or version numbers (e.g., programming languages, libraries, or solvers with versions). |
| Experiment Setup | Yes | We apply grid search to identify optimal values for each hyperparameter from {10 9, 10 8, ..., 108, 109}. The tolerance rate is set to 10 4, the maximum number of iterations to 500 for all the methods. The rank is tuned using 10 ranks varying from 5 to 40 based on relative error which we define later. In OR-MSTC and MAST, αn = 1 10N , n = 1, ..., N, η = 10 4, ρ = 1.05 and ηmax = 106. We tuned forgetting factor µ in our method and MAST based on the missing percentage. Following [Song et al., 2017], the initial completion and warm start matrices are calculated using NNCP method. |