Flexible Multi-View Representation Learning for Subspace Clustering
Authors: Ruihuang Li, Changqing Zhang, Qinghua Hu, Pengfei Zhu, Zheng Wang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on real-world datasets show that our method achieves superior clustering performance over other state-of-the-art methods. |
| Researcher Affiliation | Academia | Ruihuang Li1 , Changqing Zhang1,2 , Qinghua Hu1 , Pengfei Zhu1 and Zheng Wang1 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, China |
| Pseudocode | Yes | Algorithm 1: Optimization of our method |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for their proposed method (FMR) is openly available. |
| Open Datasets | Yes | We conduct experiments on 7 datasets from different applications: images, text, and community networks. Yale1 consists of 165 grayscale images of 15 individuals, from which 3 types of features are extracted. MSRC-v1 [Xu et al., 2016] consists of 210 images of 7 object classes, which includes 6 types of features. Notting-Hill [Wu et al., 2013] is a video face dataset consisting of 550 images of 5 main casts described from 3 different views. Reuters [Amini et al., 2009] is a multilingual dataset including 2000 newswire articles of 6 classes written in 5 languages (views). BBCSport2 is composed of news articles in 5 topical areas from BBC website, which is associated with 2 views. Football3 contains 248 English Premier League football players and clubs active on Twitter, which are described from 9 different views and associated with 20 clubs. ANIMAL [Lampert et al., 2014] contains 30475 images of 50 animal classes including 2 types of feature. 10158 samples are selected with fixed interval to generate a subset. |
| Dataset Splits | No | The paper describes the datasets used and the comparison of methods, but does not explicitly state the train/validation/test dataset splits needed to reproduce the experiment, nor does it refer to predefined splits with citations for these specific datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, or other computer specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as programming languages, libraries, or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiments, we set the dimensionality of latent representation as 200 and tune hyperparameters λ1 and λ2 from {10 5, 10 4, , 10 1, 100} and {10 10, 10 9, , 10 3, 10 2}, respectively. |