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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 201–250 of 256 papers

TitleStatusHype
Deep Multimodality Model for Multi-task Multi-view LearningCode0
Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multi-view Learning—0
Learning Common Representation from RGB and Depth Images—0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack DetectionCode0
Robust Visual Tracking using Multi-Frame Multi-Feature Joint ModelingCode0
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks—0
Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning—0
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity TypingCode0
Improving Sentence Representations with Multi-view Frameworks—0
Canonical Correlation Analysis with Implicit Distributions—0
Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization—0
ML-LocNet: Improving Object Localization with Multi-view Learning Network—0
Jointly Deep Multi-View Learning for Clustering Analysis—0
Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering—0
Integrative Multi-View Reduced-Rank Regression: Bridging Group-Sparse and Low-Rank ModelsCode0
Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views—0
Dynamic voting in multi-view learning for radiomics applications—0
Multi-view Sentence Representation Learning—0
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images—0
Dissimilarity-based representation for radiomics applications—0
Dual Memory Neural Computer for Asynchronous Two-view Sequential LearningCode0
Deep Multi-view Learning to Rank—0
A Survey on Multi-View Clustering—0
Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering—0
Scalable and Effective Deep CCA via Soft Decorrelation—0
A Survey on Multi-Task LearningCode0
Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech—0
A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams—0
Marine Animal Classification with Correntropy Loss Based Multi-view Learning—0
Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment—0
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization—0
One Size Fits Many: Column Bundle for Multi-X Learning—0
Multi-view Regularized Gaussian Processes—0
Multi-view metric learning for multi-instance image classification—0
Deep Variational Canonical Correlation Analysis—0
ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data—0
Active Regression with Adaptive Huber Loss—0
Streaming View Learning—0
Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations—0
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis—0
Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers—0
Patterns for Learning with Side InformationCode0
Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination—0
Tensor Canonical Correlation Analysis for Multi-view Dimension ReductionCode1
Multi-view learning for multivariate performance measures optimization—0
Bayesian multi-tensor factorization—0
Conditional Random Field Autoencoders for Unsupervised Structured PredictionCode0
PAC-Bayes Analysis of Multi-view Learning—0
Overlapping Trace Norms in Multi-View Learning—0
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