Biased Incomplete Multi-View Learning
Authors: Haishun Chen, Cai Xu, Ziyu Guan, Wei Zhao, Jinlong Liu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on five multi-view datasets demonstrate that RIML significantly outperforms existing methods in both accuracy and robustness. Extensive experiments on five real-world multi-view datasets demonstrate the superiority of the RIML method over existing methods, showing significant improvements in accuracy, reliability and robustness. |
| Researcher Affiliation | Academia | Haishun Chen, Cai Xu, Ziyu Guan*, Wei Zhao, Jinlong Liu School of Computer Science and Technology, Xidian University, China {chenhaishun@stu., cxu@, zyguan@, ywzhao@mail., 20069100042@stu.}xidian.edu.cn |
| Pseudocode | No | The paper describes the methodology using mathematical formulations and textual descriptions but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing source code, nor does it provide links to a code repository. |
| Open Datasets | Yes | Dataset. Hand Written (Perkins and Theiler 2003) comprises 2,000 instances of handwritten numerals ranging from 0 to 9 , which is a six-view dataset. Scene15 (Fei-Fei and Perona 2005) consists of 4,485 images associated from 15 different categories with three views. Animal (Lampert, Nickisch, and Harmeling 2013) is a animal dataset containing 10,158 instances from 50 categories, we utilize two types of features using DECAF and VGG19. Yal B (Georghiades, Belhumeur, and Kriegman 2001) is a three views dataset contains 10 categories, with a total of 650 facial images. BRCA (Wang et al. 2020a) is a three views dataset for classifying five Breast Invasive Carcinoma subtypes. |
| Dataset Splits | Yes | To create the biased incomplete multi-view dataset, we apply varying missing rates across different categories in the training set, with the highest missing rates assigned to categories with the largest number of samples. For the test set, a uniform missing rate of 0.1 is applied. ... Each dataset consists of an imbalanced training set and a balanced test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify versions for software libraries or frameworks (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | The learning rate is initialized at 0.001 and decays by a factor of 0.1 if the validation loss does not improve over five epochs. To prevent overfitting, we incorporate a dropout rate of 0.2 and employ early stopping during training. We use the same hyperparameter values across all datasets: δ = 0.21 in Eq. (7b), Ns = 10 in Eq. (9), γ = 2 in Eq. (10), and k = 10 for k-nearest neighbors. |