11 papers with code • 0 benchmarks • 3 datasets
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Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.
In many modern applications from, for example, bioinformatics and computer vision, samples have multiple feature representations coming from different data sources.
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.
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
Under this model, latent correlation maximization is shown to guarantee the extraction of the shared components across views (up to certain ambiguities).
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.
We propose iDeepViewLearn (Interpretable Deep Learning Method for Multiview Learning) for learning nonlinear relationships in data from multiple views while achieving feature selection.