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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data
Authors: Yining Wang, Yu-Xiang Wang, Aarti Singh
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present numerical results that validate our theoretical findings and compare Lasso SSC with TSC (Heckel & Bolcskei, 2013) and LRR (Liu et al., 2013). The Lasso SSC algorithm is implemented using augmented Lagrangian method (ALM) when the regularization coefficient λ is fixed and known. We also implement Lasso SSC using a solution path algorithm (Tibshirani & Taylor, 2011) to tune λ separately for each data point. The LRR implementation is obtained from (Liu, 2013). Random Gaussian projection is used for all experiments. All algorithms are implemented in Matlab. |
| Researcher Affiliation | Academia | Yining Wang EMAIL Yu-Xiang Wang EMAIL Aarti Singh EMAIL Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions 'The LRR implementation is obtained from (Liu, 2013)' but this refers to a third-party implementation, not their own. |
| Open Datasets | Yes | We start by evaluating the performance of Lasso SSC with random Gaussian projection on the extended Yale B face recognition dataset (Lee et al., 2005)... We evaluate the performance of Lasso SSC with random projection for motion trajectory segmentation on the Hopkins-155 dataset (Tron & Vidal, 2007). |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or detailed methodology for training/validation/test splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | All algorithms are implemented in Matlab. The paper does not specify a version number for Matlab or any other software dependencies. |
| Experiment Setup | Yes | The Lasso SSC algorithm is implemented using augmented Lagrangian method (ALM) when the regularization coefficient λ is fixed and known. We also implement Lasso SSC using a solution path algorithm (Tibshirani & Taylor, 2011) to tune λ separately for each data point. Random Gaussian projection is used for all experiments. |