Graph Clustering With Missing Data: Convex Algorithms and Analysis
Authors: Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our theoretical findings through extensive simulations. We also run our algorithm on a real data set obtained from crowdsourcing an image classification task on the Amazon Mechanical Turk, and observe significant performance improvement over traditional methods such as k-means. |
| Researcher Affiliation | Academia | Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi Department of Electrical Engineering California Institute of Technology, Pasadena, CA 91125 {ramya, soymak}@caltech.edu, hassibi@systems.caltech.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We used images of 3 different breeds of dogs : Norfolk Terrier (172 images), Toy Poodle (151 images) and Bouvier des Flandres (150 images) from the Standford Dogs Dataset [29]. |
| Dataset Splits | No | The paper mentions generating random graphs and using real data, but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training or validation sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper states 'We implement Program 1.1 and 1.4 using the inexact augmented Lagrange method of multipliers [28]', but it does not provide specific software names with version numbers that would be required to replicate the experiments. |
| Experiment Setup | Yes | In both the experiments, we set the regularization parameter λ = 1.01D 1 min, ensuring that Dmin > 1/λ, enabling us to focus on observing the transition around Λsucc and Λfail. We set the regularization parameter, λ = 0.49 Λsucc, ensuring that λ < Λsucc, enabling us to focus on observing the condition of success around Dmin. Further, for Program 1.4, we set the size of the cluster region, R to 0.125 times n 2 . |