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Found 6 papers, 3 papers with code
Scalable variable selection for two-view learning tasks with projection operators
With the projection operators the relationship, correlation, between sets of input and output variables can also be expressed by kernel functions, thus nonlinear correlation models can be exploited as well.
Learning primal-dual sparse kernel machines
In this paper, instead of using the traditional representer theorem, we propose to search for a solution in RKHS that has a pre-image decomposition in the original data space, where the elements don't necessarily correspond to the elements in the training set.
Entangled Kernels -- Beyond Separability
We consider the problem of operator-valued kernel learning and investigate the possibility of going beyond the well-known separable kernels.
Partial Trace Regression and Low-Rank Kraus Decomposition
The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs.
Cross-view kernel transfer
We consider the kernel completion problem with the presence of multiple views in the data.
Multi-view Metric Learning in Vector-valued Kernel Spaces
We consider the problem of metric learning for multi-view data and present a novel method for learning within-view as well as between-view metrics in vector-valued kernel spaces, as a way to capture multi-modal structure of the data.
