Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications
Authors: Debarghya Ghoshdastidar, Ambedkar Dukkipati
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | 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:{debarghya.g,ad}@csa.iisc.ernet.in |
| 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)). |