An Effective Augmented Lagrangian Method for Fine-Grained Multi-View Optimization
Authors: Yuze Tan, Hecheng Cai, Shudong Huang, Shuping Wei, Fan Yang, Jiancheng Lv
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experiments on multi-view clustering tasks across heterogeneous datasets serve to incontrovertibly showcase the effectiveness of our proposed methodology, corroborating its preeminence over incumbent state-of-the-art alternatives. |
| Researcher Affiliation | Collaboration | Yuze Tan1,2, Hecheng Cai1,2, Shudong Huang1,2*, Shuping Wei3, Fan Yang1,4, Jiancheng Lv1,2 1College of Computer Science, Sichuan University, Chengdu 610065, China 2Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, Chengdu, 610065, China 3Nuclear Power Institute of China 4Sichuan Iot DT Technology Co., Ltd. |
| Pseudocode | Yes | Algorithm 1: Augmented Lagrangian Meth Od fi Negraine D(ALMOND) Multi-view Clustering |
| Open Source Code | No | No explicit statement or link for open-source code release for the described methodology. |
| Open Datasets | Yes | As for benchmark datasets, we adopt Yale, ORL, bbcseg13of3, Cora, Cornell, and citeseer. Specifically, Yale1 https://www.kaggle.com/datasets/olgabelitskaya/yale-facedatabase The Olivetti Research Laboratory ORL2 face data set... 2https://www.kaggle.com/datasets/tavarez/the-orl-databasefor-training-and-testing bbcseg13of3 3 is a subset of BBC data set... 3http://mlg.ucd.ie/datasets/bbc.html Cora4 data set... 4https://relational.fit.cvut.cz/dataset/CORA Cornellis a subset of Web KB 5 ... 5https://starling.utdallas.edu/datasets/webkb/ citeseer6 is the citation network... 6https://relational.fit.cvut.cz/dataset/Cite Seer |
| Dataset Splits | No | No explicit details about train/validation/test splits, percentages, or sample counts are provided for the datasets. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) are mentioned for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | With the purpose of examining how different parameter settings will affect the results of clustering, we change the values of α, β and γ in the ranges [30, . . . , 80], 7e 6, . . . , 4e 5 , and [700, . . . , 1200], respectively. For simplicity, we set it as 1 from the beginning and didn t consider it as a hyper-parameter to search in our experiment. But it grows exponentially in every iteration according to the ALM principle, i.e., ρ 1.2ρ. |