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MULTI-VIEW LEARNING

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

Papers

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Showing 211–220 of 256 papers

TitleStatusHype
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech—0
A unified framework based on graph consensus term for multi-view learning—0
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning—0
Auto-Encoder based Co-Training Multi-View Representation Learning—0
Auto-weighted Multi-view Feature Selection with Graph Optimization—0
Bayesian multi-tensor factorization—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview—0
Canonical Correlation Analysis with Implicit Distributions—0
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