5 citations · 9 across the 6 of their papers we have counts for
6 papers
Scalable variable selection for two-view learning tasks with projection operators
Sandor Szedmak, Riikka Huusari, Tat Hong Duong Le +1
In this paper we propose a novel variable selection method for two-view settings, or for vector-valued supervised learning problems. Our framework is able to handle extremely large…
Learning primal-dual sparse kernel machines
Riikka Huusari, Sahely Bhadra, Cécile Capponi +2
Traditionally, kernel methods rely on the representer theorem which states that the solution to a learning problem is obtained as a linear combination of the data mapped into the r…
Entangled Kernels -- Beyond Separability
Riikka Huusari, Hachem Kadri
We consider the problem of operator-valued kernel learning and investigate the possibility of going beyond the well-known separable kernels. Borrowing tools and concepts from the f…
Partial Trace Regression and Low-Rank Kraus Decomposition
Hachem Kadri, Stéphane Ayache, Riikka Huusari +2
The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs. We here introduce an even more genera…
Cross-view kernel transfer
Riikka Huusari, Cécile Capponi, Paul Villoutreix +1
We consider the kernel completion problem with the presence of multiple views in the data. In this context the data samples can be fully missing in some views, creating missing col…
Multi-view Metric Learning in Vector-valued Kernel Spaces
Riikka Huusari, Hachem Kadri, Cécile Capponi
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, a…