Spectral Perturbation Meets Incomplete Multi-view Data
Authors: Hao Wang, Linlin Zong, Bing Liu, Yan Yang, Wei Zhou
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China 2School of Software, Dalian University of Technology, Dalian, China 3Department of Computer Science, University of Illinois at Chicago, Chicago, USA |
| Pseudocode | Yes | Algorithm 1: The proposed overall algorithm. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We perform evaluation using four complete multi-view datasets and three natural incomplete multi-view datasets. The datasets are summarized in Table 1, where the first four datasets are complete and the last three datasets are naturally incomplete. ... 2https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set 3http://www.robots.ox.ac.uk/ vgg/data/flowers/17/index.html 4https://cs.nyu.edu/ roweis/data.html 5www.cad.zju.edu.cn/home/dengcai/Data/Face Data.html 6http://mlg.ucd.ie/datasets/3sources.html 7http://mlg.ucd.ie/datasets/segment.html |
| Dataset Splits | No | The paper describes generating incomplete multi-view datasets for evaluation and uses clustering performance metrics (ACC, NMI). However, it does not specify traditional training/validation/test dataset splits as commonly used in supervised learning contexts. Clustering is typically unsupervised and evaluated on the dataset as a whole. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., CPU, GPU models, memory, or specific computing cluster configurations) used for running the experiments. |
| Software Dependencies | No | The paper mentions using MATLAB for solving Eq. (8) for the quadratic programming problem, but it does not specify version numbers for MATLAB or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | For PIC, we set the parameter β using β = β P v Qv F / I F to balance Eq. 5 and Eq. 6. Then we empirically set β = 0.1 in evaluation. The parameter study will come shortly. |