Sparse Subspace Clustering with Missing Entries
Authors: Congyuan Yang, Daniel Robinson, Rene Vidal
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real data show the advantages and disadvantages of the proposed methods, which all outperform the natural approach (low-rank matrix completion followed by sparse subspace clustering) when the data matrix is high-rank or the percentage of missing entries is large. 4. Experiments In this section, we evaluate the performance of MC+SSC, ZF+SSC, SSC-EWZF, SSC-EC, SSC-CEC, and BCDS on both synthetic data and the Hopkins 155 motion segmentation dataset (Tron & Vidal, 2007). |
| Researcher Affiliation | Academia | Congyuan Yang YANGCY@JHU.EDU Daniel Robinson DANIEL.P.ROBINSON@JHU.EDU Ren e Vidal RVIDAL@CIS.JHU.EDU Johns Hopkins University, 3400 N Charles St., Baltimore, MD, USA |
| Pseudocode | No | The paper describes algorithms in text, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We evaluate different methods on the Hopkins 155 data set, which contains 155 video sequences with 2 or 3 moving objects. (Tron & Vidal, 2007) |
| Dataset Splits | No | The paper does not explicitly define or provide details about a validation set or split for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions methods like ADMM and LASSO, but does not provide specific software dependencies or version numbers. |
| Experiment Setup | Yes | All algorithms involve a penalty parameter λ that should be carefully chosen so as to balance reconstruction error and sparsity: a small λ may lead to sparse solutions, but a large reconstruction error, while a large λ may give very good reconstruction, but non sparse solutions. In (Elhamifar & Vidal, 2013), an adaptive choice for λ in (5) is given for a complete data matrix X as: λ = α/ min j max i =j |X X|ij, where α 1 is a new tuning parameter. |