MULTI-VIEW LEARNING
53 papers with code • 0 benchmarks • 1 datasets
Multi-View Learning is a machine learning framework where data are represented by multiple distinct feature groups, and each feature group is referred to as a particular view.
Source: Dissimilarity-based representation for radiomics applications
Benchmarks
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Libraries
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CarFusion
Latest papers
Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data
To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF).
Deep brain state classification of MEG data
SMehrkanoon/Deep-brain-state-classification-of-MEG-data
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The experimental results of cross subject multi-class classification on the studied MEG dataset show that the inclusion of attention improves the generalization of the models across subjects.
Deep Tensor CCA for Multi-view Learning
We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order.
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB
Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely.
Learning Autoencoders with Relational Regularization
HongtengXu/Relational-AutoEncoders
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ICML 2020
A new algorithmic framework is proposed for learning autoencoders of data distributions.
Dual Adversarial Domain Adaptation
Recent experiments have shown that when the discriminator is provided with domain information in both domains and label information in the source domain, it is able to preserve the complex multimodal information and high semantic information in both domains.
CPM-Nets: Cross Partial Multi-View Networks
hanmenghan/CPM_Nets
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NeurIPS 2019
Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing.
Multi-View Broad Learning System for Primate Oculomotor Decision Decoding
Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source.
Deep Multi-View Learning via Task-Optimal CCA
hdcouture/TOCCA
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Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use of class labels.
Neural News Recommendation with Attentive Multi-View Learning
In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning.
