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Found 6 papers, 1 papers with code
Learning Canonical F-Correlation Projection for Compact Multiview Representation
With this framework as a tool, we propose a correlative covariation projection (CCP) method by using an explicit nonlinear mapping.
The Emerging Trends of Multi-Label Learning
Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data.
Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space.
A Survey on Multi-output Learning
Multi-output learning aims to simultaneously predict multiple outputs given an input.
When Gaussian Process Meets Big Data: A Review of Scalable GPs
The review of scalable GPs in the GP community is timely and important due to the explosion of data size.
Sparse Embedded k-Means Clustering
For example, compared to the advanced singular value decomposition based feature extraction approach, [1] reduce the running time by a factor of $\min \{n, d\}\epsilon^2 log(d)/k$ for data matrix $X \in \mathbb{R}^{n\times d} $ with $n$ data points and $d$ features, while losing only a factor of one in approximation accuracy.
