Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
Authors: Debarghya Ghoshdastidar, Ambedkar Dukkipati
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also complement our theoretical findings by elaborate empirical comparison of various hypergraph partitioning schemes. Section 6 presents a wide variety of empirical studies that (i) validate our theoretical findings regarding relative merits of TTM over previously studied algorithms, (ii) compare spectral methods to other hypergraph partitioning algorithms, including popular hMETIS tool (Karypis and Kumar, 2000), and (iii) weigh the merits of hypergraph partitioning in subspace clustering applications, including benchmark problems. |
| Researcher Affiliation | Academia | Debarghya Ghoshdastidar EMAIL Ambedkar Dukkipati EMAIL Department of Computer Science & Automation Indian Institute of Science Bangalore 560012, India |
| Pseudocode | Yes | Algorithm TTM : Spectral relaxation of tensor trace maximisation problem; Algorithm Sampled TTM : TTM where a sampled set of edge weights are observed; Algorithm Tetris : TTM with iterative sampling for subspace clustering |
| Open Source Code | Yes | Codes are available at: http://sml.csa.iisc.ernet.in/SML/code/Feb16Tensor Trace Max.zip and also on the personal webpage of first author. |
| Open Datasets | Yes | The Hopkins 155 database (Tron and Vidal, 2007) contains a number of videos capturing motion of multiple objects or rigid bodies. |
| Dataset Splits | No | No explicit training/test/validation splits are provided. For synthetic data, the paper describes generating 'n/k points' from 'k=5 subspaces'. For Hopkins 155, it mentions '120 sequences, each containing two motions, and 35 three motion sequences' but no explicit splits are detailed. |
| Hardware Specification | Yes | We note that the reported time is based on the fact that we have used Matlab implementations of the algorithms, run on a Mac OS X operating system with 2.2 GHz Intel Core i7 processor and 16 GB memory. |
| Software Dependencies | No | The paper mentions 'Matlab implementations' and 'Mac OS X operating system' but does not provide specific version numbers for Matlab or any other libraries/solvers. |
| Experiment Setup | Yes | For existing methods, we fix the parameters as mentioned in Park et al. (2014). For Tetris and SGC, the parameters are set to the same values as SCC, where c = 100k and σ as in (31) is determined by the algorithm. In case of uniformly sampled TTM, we fix σ to be same as the value determined by Tetris. |