Unsupervised Feature Selection on Networks: A Generative View
Authors: Xiaokai Wei, Bokai Cao, Philip S. Yu
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three real-world datasets show that our approach can select more discriminative features than state-of-the-art methods. |
| Researcher Affiliation | Academia | Xiaokai Wei , Bokai Cao and Philip S. Yu Department of Computer Science, University of Illinois at Chicago, IL, USA Institute for Data Science, Tsinghua University, Beijing, China {xwei2,caobokai,psyu}@uic.edu |
| Pseudocode | Yes | Algorithm 1 Alternating Optimization with Projected Gradient Descent |
| Open Source Code | No | The paper mentions using a K-means code available at a URL, but it does not provide access to the authors' own source code for the proposed GFS method. |
| Open Datasets | Yes | We use three publicly available network datasets with node attributes: Citeseer dataset, Cora Dataset and Wikipedia dataset 1 (Sen et al. 2008). |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. It mentions using K-means for evaluation and repeating experiments for 20 times, but no specific splits are detailed. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., CPU, GPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper mentions using 'K-means' code from a given URL but does not provide specific version numbers for K-means or any other software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | For the proposed method GFS, we found it is not sensitive to the parameters in a reasonable range. So we fix the parameters of GFS for all datasets with β = 1 and λ = 1. |