Clustering-Induced Adaptive Structure Enhancing Network for Incomplete Multi-View Data
Authors: Zhe Xue, Junping Du, Changwei Zheng, Jie Song, Wenqi Ren, Meiyu Liang
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmark datasets demonstrate that our method can comprehensively obtain the structure of incomplete multi-view data and achieve superior performance compared to the other methods. |
| Researcher Affiliation | Academia | 1 Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China 2 State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China |
| Pseudocode | Yes | Algorithm 1: The learning procedure of CASEN. Algorithm 2: The learning procedure of MKC. |
| Open Source Code | No | The paper states 'CASEN is implemented in Py Torch and executed on an Ubuntu 18.04 machine with Nvidia Ge Force RTX 2080 Ti GPU.' but does not provide a link or explicit statement about the public availability of its source code. |
| Open Datasets | Yes | We adopt four well-known multi-view learning datasets to demonstrate the effectiveness of the proposed method. 1) BBC: It consists of 685 documents from BBC news website which corresponds to stories about five topical areas. Each sample is described by four views [Greene and Cunningham, 2006]. 2) Caltech20: It is a subset of Caltech101 dataset, which consists of 2386 images of 20 classes. To obtain multiple views, we manually extract six kinds of visual features as in [Cai et al., 2013]. 3) Wikipedia: It contains 2866 multimedia documents which are collected from Wikipedia [Rasiwasia et al., 2010]. Each document contains two views i.e., the image view and the text view. 4) MNIST: It is composed of 10000 samples of ten digits. Pixel feature and edge feature are adopted as two views [Le Cun et al., 1998]. |
| Dataset Splits | No | The paper mentions how incomplete multi-view data is constructed by randomly removing instances (e.g., 'randomly remove p% instances from each view'), but it does not specify explicit train/validation/test dataset splits, percentages, or absolute counts for model training and evaluation. |
| Hardware Specification | Yes | CASEN is implemented in Py Torch and executed on an Ubuntu 18.04 machine with Nvidia Ge Force RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the implementation framework and 'Ubuntu 18.04' as the operating system, but it does not provide specific version numbers for PyTorch or other key software dependencies. |
| Experiment Setup | Yes | The autoencoders f (v) and g(v) are stacked by four layers and the dimensions are with [0.8mv, 0.8mv, 1200, 50] and [50, 0.8mv, 0.8mv, mv], respectively. We adopt two convolution layers in GCN and the dimensions are [0.8mv, 50]. The FC layers in LC are designed with four layers [l, d1, d2, d3], l is the dimension of the input layer, d1 and d2 are the dimensions of hidden layers, d3 is the dimension of output layer. We set d1 = n, d2 = 0.8n, d3 = c. The other parameters are set as η1 = 0.1, η2 = 0.01, λ = 0.5, θ = 0.1, r = 2. Rectified Linear Unit (ReLU) is adopted as the activation function of our network. Stochastic gradient descent (SGD) is adopted to pretrain the multi-view autoencoders. |