Multiview Learning

8 papers with code • 0 benchmarks • 2 datasets

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Most implemented papers

Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters

goyalanil/Multiview_Dataset_MNIST 17 Aug 2018

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

mehmetgonen/lmkkmeans NeurIPS 2014

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

SeojinBang/MKKC 6 Mar 2018

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 17 Apr 2018

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

goyalanil/Multiview_Dataset_MNIST 25 May 2018

We tackle the issue of classifier combinations when observations have multiple views.

Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing

yyaghoobzadeh/MVET EMNLP 2018

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 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 2 Dec 2021

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.