Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion
Authors: Quanming Yao, James T. Kwok
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and large recommendation data sets show that the proposed algorithm is much faster than Soft-Impute and other state-of-the-art matrix completion algorithms. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay, Hong Kong |
| Pseudocode | Yes | Algorithm 1 Power Method( Z, R, ϵ). Algorithm 2 Algorithm to approximate the SVT of Zt (approx-SVT( Zt, R, λ, ϵ)). Algorithm 3 Accelerated Inexact Soft-Impute (AIS-Impute). |
| Open Source Code | No | The paper provides links to the code for *compared* methods (APG, Soft-Impute) in footnotes 1 and 2, but does not provide a link or explicit statement for the open-sourcing of the proposed AIS-Impute method. |
| Open Datasets | Yes | Movie Lens data set3 (Table 2) contains ratings (from 1 to 5) of different users on movies, and has been commonly used in matrix completion experiments [Mazumder et al., 2010; Hsieh and Olsen, 2014]. Following [Wang et al., 2014], we use 50% of the observed ratings for training, 25% for validation and the rest for testing. Footnote 3: http://grouplens.org/datasets/movielens/ |
| Dataset Splits | Yes | Half of them are used for training, and the other half as validation set for parameter tuning. Testing is performed on the unobserved (missing) elements. we use 50% of the observed ratings for training, 25% for validation and the rest for testing. |
| Hardware Specification | Yes | Experiments are performed on a PC with Intel i7 CPU and 16GB RAM. |
| Software Dependencies | No | APG and AIS-Impute are in Matlab, while Soft-Impute is in R (and is called from Matlab). No specific version numbers for Matlab or R are provided. |
| Experiment Setup | Yes | Require: partially observed matrix O, parameter λ, decay parameter ν (0, 1), threshold ϵ; For further speedup, at step 5, we employ a continuation strategy as in [Toh and Yun, 2010; Mazumder et al., 2010], in which λt is initialized to a large value and then decreased gradually. Moreover, as in [O Donoghue and Candes, 2012; Nesterov, 2013], the algorithm is restarted (at step 12) if F(X) starts to increase. |