Deep Subspace Clustering with Sparsity Prior
Authors: Xi Peng, Shijie Xiao, Jiashi Feng, Wei-Yun Yau, Zhang Yi
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify the effectiveness of our method. |
| Researcher Affiliation | Collaboration | 1Institute for Infocomm Research, Singapore; 2Nanyang Technological University, Singapore; 3Omni Vision Technologies Singapore Pte. Ltd.; 4National University of Singapore, Singapore 5College of Computer Science, Sichuan University, Chengdu, P. R. China. |
| Pseudocode | Yes | Algorithm 1 summarizes the detailed procedure for optimizing PARTY. Algorithm 1 Deep Subspace Clustering with Sparsity Prior |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Data Sets: We carry our experiments using three real-world data sets, i.e. Extended Yale database B (Yale B) [Georghiades et al., 2001], COIL20 image data set [Nene et al., 1996], and the BF0502 data set [Sivic et al., 2009]. |
| Dataset Splits | No | The paper mentions training models ('In all experiments, we train the SAE and our PARTY with five layers') but does not specify details about training, validation, or test splits such as percentages, sample counts, or methodology for partitioning the datasets. |
| Hardware Specification | No | The paper mentions training neural networks but does not provide specific hardware details such as GPU or CPU models, memory, or cloud computing instance types used for the experiments. |
| Software Dependencies | No | The paper mentions using a '1-solver (i.e. the Homotopy solver [Yang et al., 2010])' but does not specify any software names with version numbers for other dependencies (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | In all experiments, we train the SAE and our PARTY with five layers at which consists of 300-200-150-200-300 neurons. ... For our PARTY with the tradeoff parameters, λ1 and λ2. We fix λ2 = 10 3 for all data sets and experimentally choose λ1. |