Multi-view Granular-ball Contrastive Clustering
Authors: Peng Su, Shudong Huang, Weihong Ma, Deng Xiong, Jiancheng Lv
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to validate the effectiveness of the proposed method. Extensive experiments on seven typical multi-view datasets demonstrate that our method achieves comparable or superior performance compared to state-of-the-art methods. |
| Researcher Affiliation | Academia | 1College of Computer Science, Sichuan University, Chengdu 610065, China 2Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China 3Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China 4Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030, USA EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using text, equations, and figures, but it does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/Duo-laimi/mgbcc main |
| Open Datasets | Yes | BBCSport (Greene and Cunningham 2006) includes 544 sports news articles in 5 subject areas, with 3183-dimensional MTX features and 3203-dimensional TERMS features, forming 2 views. Caltech101-20 (Li et al. 2015) contains 101 classes in total. We select 20 widely used classes with 2 views and 2386 samples for our experiments. Cora (Bisson and Grimal 2012) contains 4 views, including content, inbound, outbound, and citations, extracted from the documents. Scene-15 (Fei-Fei and Perona 2005) consists of 4568 natural scenes categorized into 15 groups. Each scene is described by three types of features: GIST, SIFT, and LBP. MNIST-USPS (Peng et al. 2019) is a popular handwritten digit dataset containing 5000 samples with two different styles of digital images. ALOI-100 (Schubert and Zimek 2010) consists of 10800 object images, with each image described by 4 different features. Noisy MNIST (Wang et al. 2015) uses the original images as view 1 and randomly selects within-class images with white Gaussian noise as view 2. |
| Dataset Splits | No | The paper lists several datasets used but does not explicitly provide details on training, validation, or test splits. It mentions applying k-means to obtain clustering labels after fusing features, which is typical for unsupervised learning, but does not specify splits for the deep model training itself or how performance metrics were calculated with respect to data partitioning. |
| Hardware Specification | Yes | Our implementation of MGBCC is carried out using Py Torch 2.3 (Paszke et al. 2019) on a Windows 10 operating system, powered by an NVIDIA Ge Force GTX 1660 Ti GPU. |
| Software Dependencies | Yes | Our implementation of MGBCC is carried out using Py Torch 2.3 (Paszke et al. 2019) on a Windows 10 operating system, powered by an NVIDIA Ge Force GTX 1660 Ti GPU. |
| Experiment Setup | Yes | The network structure follows a standard autoencoder architecture. For each view, the encoder consists of several linear layers with Re LU activation functions between each pair of layers. Except for the BBCSport and Cora datasets, all other datasets use the same encoder structure with dimensions set as {dv, 2000, 500, 500, d}, where dv is the input feature dimension of each view. d is the projection feature dimension, which is the same for all views. After encoding, inputs undergo standardization. The decoder mirrors the encoder structure. For the Cora dataset, we use the same dimensions but without activation functions between layers, resulting in a linear projection. For BBCSport, given its small sample size of 544, we use a single-layer linear projection with the encoder dimensions set to {dv, d}. We employ the Adam optimizer with learning rate of 0.0001 and weight decay of 0. The batch size is typically set to either 256 or 1024, depending on the dataset size. The regularization parameter λ is generally set to 1 across most datasets, except for BBCSport and Cora, where it is adjusted to 0 due to differences in the projection approach (e.g., linear or nonlinear). The threshold parameter τ is uniformly set to 0.1 across all datasets. The granularity parameter p significantly impacts the experimental results, which will be analyzed later. |