Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model
Authors: Debarghya Ghoshdastidar, Ambedkar Dukkipati
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the claims of Corollaries 2 and 3, where we stated that for higher order tensors, the number of misclustered nodes decays to zero at a faster rate. We run Algorithm 1 on both the models of subspace clustering and point-set matching, varying the number of nodes n, the results for each n being averaged over 10 trials.We now turn to practical applications, and test the performance of Algorithm 1 in motion segmentation. We perform the experiments on the Hopkins 155 dataset [24] |
| Researcher Affiliation | Academia | Debarghya Ghoshdastidar Ambedkar Dukkipati Department of Computer Science & Automation Indian Institute of Science Bangalore 560012, India EMAIL |
| Pseudocode | Yes | Algorithm 1 Spectral partitioning of m-uniform hypergraph 1. From the mth-order af๏ฌnity tensor An, construct b An using (2). 2. Let Wn = b An b AT n, and Dn Rn n be diagonal with (Dn)ii = Pn j=1(Wn)ij. 3. Set Ln = D 1/2 n Wn D 1/2 n . 4. Compute leading k orthonormal eigenvectors of Ln, denoted by matrix Xn Rn k. 5. Cluster the rows of Xn into k clusters using k-means clustering. 6. Assign node-i of hypergraph to jth partition if ith row of Xn is grouped in jth cluster. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | We perform the experiments on the Hopkins 155 dataset [24] and We now consider a matching problem using points sampled from images in Mpeg-7 database [25]. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts for train/validation/test sets) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For the clustering model (Section 2.1), we choose p = 0.6, q = 0.4, and consider two cases of k = 2 and 3 cluster problems. [...] for the matching problem (Section 2.2), where we use p = 0.9, q = 0.1 and the number of correct matches as n. [...] We used 4th-order tensors in the approach, where the large dimensionality of b An is tackled by using only 500 uniformly sampled columns of b An for computing Wn. |