Lifelong Multi-view Spectral Clustering
Authors: Hecheng Cai, Yuze Tan, Shudong Huang, Jiancheng Lv
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our proposed method outperforms other competitive clustering methods on multi-view datasets by a large margin.In this section, we evaluate the clustering performance of our LMSC model via a throughout empirical comparisons. Starting with a brief introduction to the benchmark datasets and several SOTA methods we adopted, we demonstrate the clustering results and followed by convergence analysis and parameter analysis of our model. |
| Researcher Affiliation | Academia | Hecheng Cai , Yuze Tan , Shudong Huang and Jiancheng Lv College of Computer Science, Sichuan University, Chengdu, China {caihecheng, yuzetan}@stu.scu.edu.cn, {huangsd, lvjiancheng}@scu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Lifelong multi-view spectral clustering (LSMC) model |
| 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 | Aiming at thoroughly examining the clustering performance of our method, several real-world datasets are utilized in our experiment. All datasets are divided into two, three, or four task groups such that each task contains all clusters. 3Sources comprises of 3 common online news sources i.e., The Guardian, Reuters, and BBC. 169 different news stories are gathered from the agencies. BBC dataset is composed of news stories in five different labels: politics, entertainment, business, tech and sport. We use 685 samples from 4 sources. BBCSport contains 544 archives collected from the BBCSport website, where each document is divided into 2 kinds of features. Cornell dataset is a popular benchmark for multi-view clustering. |
| Dataset Splits | No | The paper mentions dividing datasets into task groups but does not provide specific details on training, validation, or test dataset splits (percentages, counts, or explicit standard split references) for reproducibility. |
| Hardware Specification | Yes | The experimental environment of the paper is AMD Ryzen 5 2600X, Windows 10 Operating System, 16 GB Main Memory, and the experimental platform is MATLAB R2022b. |
| Software Dependencies | Yes | The experimental environment of the paper is AMD Ryzen 5 2600X, Windows 10 Operating System, 16 GB Main Memory, and the experimental platform is MATLAB R2022b. |
| Experiment Setup | Yes | To explore the effect on three parameters in Eq. (8), we tune λ, µ and β within the range [1e 3, 1e 1, . . . , 1e3]. We see the parameters of LMSC are tuned roughly. Better parameter tuning would achieve better clustering performance than that recorded in this paper. each approach is conducted ten times on every dataset with several tasks. The average values of each task and all tasks are adopted. |