Decentralized Robust Subspace Clustering

Authors: Bo Liu, Xiao-Tong Yuan, Yang Yu, Qingshan Liu, Dimitris Metaxas

AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We evaluate our algorithm on a shared-memory architecture. Experimental results on real-world datasets confirm that the proposed block-wise ADMM framework is substantially more efficient than its matrix counterpart used by SSC, without sacrificing accuracy.
Researcher Affiliation Academia Department of Computer Science, Rutgers, The State University of New Jersey Jiangsu Province Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology
Pseudocode Yes Algorithm 1: The processing of ADMM-DSSC when calculating Ci and Ei.
Open Source Code No The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described.
Open Datasets Yes We test DSSC-ADMM on the hand written digits image dataset MINIST (Le Cun et al. 1998). ... CIFAR10 is composed of 60000 images from 10 categories (Krizhevsky and Hinton 2009).
Dataset Splits No The paper mentions selecting 5000 or 1500 images per digit for datasets but does not explicitly provide training/validation/test dataset splits with percentages, sample counts, or predefined split citations for reproducibility.
Hardware Specification Yes The algorithm is implemented in Matlab and tested on a cluster with 2.7GHz CPUs and 128GB RAM.
Software Dependencies No The paper states 'The algorithm is implemented in Matlab' but does not specify a version number for Matlab or any other key software components used in the experiments.
Experiment Setup Yes The optimization termination criterion is set as C(t) C(t 1) T 0.01, E(t) E(t 1) T 0.01 or 200 iterations is reached. ... We empirically set λ1 = 0.67. m is chosen to be 2,3,4,5 and 6, respectively. ... The parameters λ1 and λ2 are set to be 0.67 and 1.5, respectively.