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..
Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications
Authors: Debarghya Ghoshdastidar, Ambedkar Dukkipati
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on geometric grouping and motion segmentation demonstrate the practical significance of the proposed methods. In this section, we conduct experiments on geometric grouping and motion segmentation. |
| Researcher Affiliation | Academia | Debarghya Ghoshdastidar and Ambedkar Dukkipati Department of Computer Science & Automation Indian Institute of Science Bangalore 560012, India email:EMAIL |
| Pseudocode | Yes | Algorithm 1 Clustering using m-ary affinity relations, Algorithm 2 Column sampling variant of Algorithm 1, Algorithm 3 Nystr om approximation of Algorithm 1 |
| Open Source Code | No | The paper does not provide any explicit statements about the availability of source code or links to a code repository. |
| Open Datasets | Yes | Finally, we conduct experiments on the Hopkins 155 motion segmentation database (Tron and Vidal 2007), where each video contains two or three independent motions. |
| Dataset Splits | No | The paper mentions using the 'Hopkins 155 motion segmentation database' but does not provide specific details on how this dataset was split into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific details on ancillary software, such as programming languages, libraries, or solvers with version numbers, used for implementation. |
| Experiment Setup | Yes | For geometric grouping... The m-way affinity is simply e cf for some parameter c > 0. For motion segmentation... we use 4th-order tensors, and fit group of four trajectories in a subspace of dimension 2. The affinities are of the form e cf( ), where f( ) is the fitting error. Algorithm 2... Number of sampled columns c; Threshold parameter τ > 0. Algorithm 3... Number of initial clusters kr; Number of vertices chosen from each cluster nr( (m 1)). |