Online Convex Matrix Factorization with Representative Regions
Authors: Jianhao Peng, Olgica Milenkovic, Abhishek Agarwal
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real world datasets show significant computational savings of the proposed online convex MF method compared to classical convex MF. |
| Researcher Affiliation | Academia | Abhishek Agarwal Electrical and Computer Engineering University of Illinois Urbana-Champaign abhiag@illinois.edu Jianhao Peng Electrical and Computer Engineering University of Illinois Urbana-Champaign jianhao2@illinois.edu Olgica Milenkovic Electrical and Computer Engineering University of Illinois Urbana-Champaign milenkov@illinois.edu |
| Pseudocode | Yes | Algorithm 1 Initialization; Algorithm 2 Online cvx MF |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of its source code. |
| Open Datasets | Yes | The real world datasets include are taken from the UCI Machine Learning [30] and the 10X Genomics repository [31]. |
| Dataset Splits | Yes | After performing cross validation on an evaluation set of size 1000, we selected c= 0.2. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. It only discusses the datasets and algorithms. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | After performing cross validation on an evaluation set of size 1000, we selected c= 0.2. ... All algorithms ran 3,000 iterations with N=150 and λ=0.1 to generate eigenimages, capturing the characteristic features used as bases [35]. |