Greedy Subspace Clustering
Authors: Dohyung Park, Constantine Caramanis, Sujay Sanghavi
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 dhpark@utexas.edu Constantine Caramanis Dept. of Electrical and Computer Engineering The University of Texas at Austin constantine@utexas.edu Sujay Sanghavi Dept. of Electrical and Computer Engineering The University of Texas at Austin sanghavi@mail.utexas.edu |
| 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-flats, and the K-means used in the spectral clustering are all fixed to 10. NSN with K = kmax = d followed by GSR with arbitrarily small . |