COMIC: Multi-view Clustering Without Parameter Selection

By Xi Peng, Zhenyu Huang, Jiancheng Lv, Hongyuan Zhu, Joey Tianyi Zhou. International Conference on Machine Learning, 2019

358 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Robust Multi-View Clustering With Incomplete Information (2022, methodology): “• MNIST-USPS : Following [50], the USPS and MNIST datasets are used as two views.”
Multi-level Feature Learning for Contrastive Multi-view Clustering (2021, unknown): “MNIST-USPS [34] is a popular handwritten digit dataset, which contains 5,000 samples with two different styles of digital images.”
COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (2021, background): “To achieve the end, the key is exploring the consistency across different views so that a common/shared representation is learned [5,12,14,21,33,47].”
Dual Contrastive Prediction for Incomplete Multi-View Representation Learning (2022, unknown): “like clustering [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], classification [14], [15], [16], [17], and human action recognition [18].”
Trusted Multi-View Classification With Dynamic Evidential Fusion (2022, methodology): “These methods are typically built on complex models [1], [2], [3], [4], [5], [6], [7] (usually integrating differ-”