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 [1].
Greedy Subspace Clustering
Authors: Dohyung Park, Constantine Caramanis, Sujay Sanghavi
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic data generated from the standard unions of subspaces model demonstrate our theory. We also show that our algorithm performs competitively against state-of-the-art algorithms on realworld applications such as motion segmentation and face clustering, with much simpler implementation and lower computational cost. |
| Researcher Affiliation | Academia | Dohyung Park Dept. of Electrical and Computer Engineering The University of Texas at Austin EMAIL Constantine Caramanis Dept. of Electrical and Computer Engineering The University of Texas at Austin EMAIL Sujay Sanghavi Dept. of Electrical and Computer Engineering The University of Texas at Austin EMAIL |
| Pseudocode | Yes | Algorithm 1 Nearest Subspace Neighbor (NSN) Input: A set of N samples Y = {y1, . . . , y N}, The number of required neighbors K, Maximum subspace dimension kmax. Output: A neighborhood matrix W 2 {0, 1}N N |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for the methods (NSN+GSR, NSN+Spectral) described in this paper. |
| Open Datasets | Yes | For the motion segmentation, we used Hopkins155 dataset [17], which contains 155 video sequences of 2 or 3 motions. For the face clustering, we used Extended Yale B dataset with cropped images from [5, 13]. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits (e.g., percentages, sample counts, or explicit instructions for generating them) for reproduction. |
| Hardware Specification | No | The paper mentions running experiments 'on a single desktop' but does not provide specific details about the hardware components (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions that MATLAB codes for comparative algorithms were used, but it does not specify specific software dependencies with version numbers for the authors' proposed algorithms (NSN+GSR, NSN+Spectral). |
| Experiment Setup | Yes | For NSN, we used the fast implementation described in Section A.1. The numbers of replicates in K-means, K-๏ฌats, and the K-means used in the spectral clustering are all ๏ฌxed to 10. NSN with K = kmax = d followed by GSR with arbitrarily small . |