Multiview Learning
14 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Multiview Learning
MTL-AQA
Multimodal PISA
MNIST Multiview Datasets
Latest papers with no code
Application of multiview techniques to NHANES dataset
Disease prediction or classification using health datasets involve using well-known predictors associated with the disease as features for the models.
Forward Stagewise Additive Model for Collaborative Multiview Boosting
Also, the proposed model is compared with traditional boosting and recent multiview boosting algorithms.
PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach
We study a two-level multiview learning with more than two views under the PAC-Bayesian framework.
Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier
We define an intact vector for each data point, and a view-conditional transformation matrix for each view, and propose to reconstruct the multiple view feature vectors by the product of the corresponding intact vectors and transformation matrices.
Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs
Matrix factorization (MF) and Autoencoder (AE) are among the most successful approaches of unsupervised learning.
Sherlock: Scalable Fact Learning in Images
We show that learning visual facts in a structured way enables not only a uniform but also generalizable visual understanding.
Multiview Hessian Discriminative Sparse Coding for Image Annotation
In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation.
Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis
To unify these two tasks, we present a new sparse Bayesian approach for joint association study and disease diagnosis.
Accelerated Training for Matrix-norm Regularization: A Boosting Approach
Sparse learning models typically combine a smooth loss with a nonsmooth penalty, such as trace norm.
