Multiview Learning
8 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Multiview Learning
MTL-AQA
Multimodal PISA
Most implemented papers
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.
Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology
In many modern applications from, for example, bioinformatics and computer vision, samples have multiple feature representations coming from different data sources.
Robust Multiple Kernel k-means Clustering using Min-Max Optimization
To address this problem and inspired by recent works in adversarial learning, we propose a multiple kernel clustering method with the min-max framework that aims to be robust to such adversarial perturbation.
Deep Multimodal Subspace Clustering Networks
mahdiabavisani/Deep-multimodal-subspace-clustering-networks
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In addition to various spatial fusion-based methods, an affinity fusion-based network is also proposed in which the self-expressive layer corresponding to different modalities is enforced to be the same.
Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization
We tackle the issue of classifier combinations when observations have multiple views.
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
For representation, we consider representations based on the context distribution of the entity (i. e., on its embedding), on the entity's name (i. e., on its surface form) and on its description in Wikipedia.
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective
llvqi/multiview_and_self-supervision
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ICLR 2022
Under this model, latent correlation maximization is shown to guarantee the extraction of the shared components across views (up to certain ambiguities).
Stationary Diffusion State Neural Estimation for Multiview Clustering
kunzhan/sdsne
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Meanwhile, instead of using auto-encoder in most unsupervised learning graph neural networks, SDSNE uses a co-supervised strategy with structure information to supervise the model learning.
