A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data
Authors: Yining Wang, Yu-Xiang Wang, Aarti Singh
ICML 2015 | Conference PDF | Archive PDF | Plain Text | 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 YININGWA@CS.CMU.EDU Yu-Xiang Wang YUXIANGW@CS.CMU.EDU Aarti Singh AARTI@CS.CMU.EDU 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. |