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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit
Authors: Jie Shen, Ping Li, Huan Xu
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic and realistic datasets further substantiate that our algorithm is fast, robust and memory efficient. |
| Researcher Affiliation | Academia | Jie Shen EMAIL Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA; Ping Li EMAIL Dept. of Statistics & Biostatistics, Dept. of Computer Science, Rutgers University, Piscataway, NJ 08854, USA; Huan Xu EMAIL Department of Industrial and Systems Engineering, National University of Singapore, Singapore |
| Pseudocode | Yes | Algorithm 1 Online Low-Rank Subspace Clustering |
| Open Source Code | No | The paper does not provide any links to open-source code or explicit statements about code availability. |
| Open Datasets | Yes | Datasets. We examine the performance for subspace clustering on 5 realistic databases shown in Table 1, which can be downloaded from the Lib SVM website. For MNIST, We randomly select 20000 samples to form MNIST-20K since we find it time consuming to run the batch methods on the entire database. Table 1. Datasets for subspace clustering. #classes #samples #features Mushrooms 2 8124 112 DNA 3 3186 180 Protein 3 24,387 357 USPS 10 9298 256 MNIST-20K 10 20,000 784 |
| Dataset Splits | No | The paper mentions datasets used for experiments but does not specify how they were split into training, validation, and test sets, beyond mentioning MNIST-20K was formed by random selection. No percentages or counts for splits are given. |
| Hardware Specification | No | The paper states that PCP utilizes a highly optimized C++ toolkit while their algorithms are fully written in Matlab, but it does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running their experiments. |
| Software Dependencies | No | The paper mentions their algorithms are "fully written in Matlab" but does not specify a version number or any other software dependencies with versions. |
| Experiment Setup | Yes | Parameters. We set λ1 = 1, λ2 = 1/ p and λ3 = t/p, where t is the iteration counter. These settings are actually used in ORPCA. We follow the default parameter setting for the baselines. |