Multi-view learning is a machine learning framework in which data is represented by multiple distinct feature groups, each of which is called a specific view. The multi-view-based method is …

In this paper, we study multi-view classification in the area of machine learning as one way to improve classification performance. Thereby, view is meant literal, i.e., each view is a distinct …

This project solves a multi-view image classification problem using two different approaches : White-box feature extractors (e.g. SIFT) and clustering for image quantization, combined with …

We classify current theories on multi-view learning into four categories which are CCA, effectiveness of co-training, generalization error analysis for co-training, and general-ization …

This book describes the models and algorithms of multiview learning in real data analysis. Incorporating multiple views to improve the generalization performance, multiview learning is …

Dec 14, 2024 · Despite its potential advantages, multi-view learning poses several challenges, including view inconsistency, view complementarity, optimal view fusion, the curse of …

Although feature learning works well on static images, in a huge number of computer vision tasks, it is the relationship between images not the content of any single image that carries the …

Abstract: In this paper, we propose a new deep metric learning approach, called MVDML, for multi-view image classification. Multi-view features can provide more information than single …

Dec 15, 2020 · In this paper, we propose a novel MDAL framework for large-scale image classification tasks. To the best of our knowledge, we are the first to incorporate deep learning …

By leveraging multi-view learning, GNNs can combine multiple perspectives of an object, such as spatial features, temporal dynamics, and object relationships, to significantly improve the …