English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently, classical kernel methods have been extended by the introduction ofsuitable tensor kernels so to promote sparsity in the solution of theunderlying regression problem. Indeed, they solve an lp-norm regularizationproblem, with p=m/(m-1) and m even integer, which happens to be close to alasso problem. However, a major drawback of the method is that storing tensorsrequires a considerable amount of memory, ultimately limiting itsapplicability. In this work we address this problem by proposing two advances.First, we directly reduce the memory requirement, by intriducing a new and moreefficient layout for storing the data. Second, we use a Nystrom-typesubsampling approach, which allows for a training phase with a smaller numberof data points, so to reduce the computational cost. Experiments, both onsynthetic and read datasets, show the effectiveness of the proposedimprovements. Finally, we take case of implementing the cose in C++ so tofurther speed-up the computation.

Efficient Tensor Kernel methods for sparse regression