Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Sparse Subspace Clustering with Missing Entries
Authors: Congyuan Yang, Daniel Robinson, Rene Vidal
ICML 2015 | Venue PDF | LLM Run Details | Input Tokens: 18,624 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,296 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Daniel Robinson EMAIL Ren e Vidal EMAIL 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. |