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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Kernel methods are widespread in machine learning; however, they are limitedby the quadratic complexity of the construction, application, and storage ofkernel matrices. Low-rank matrix approximation algorithms are widely used toaddress this problem and reduce the arithmetic and storage cost. However, weobserved that for some datasets with wide intra-class variability, the optimalkernel parameter for smaller classes yields a matrix that is less wellapproximated by low-rank methods. In this paper, we propose an efficientstructured low-rank approximation method -- the Block Basis Factorization (BBF)-- and its fast construction algorithm to approximate radial basis function(RBF) kernel matrices. Our approach has linear memory cost and floating-pointoperations for many machine learning kernels. BBF works for a wide range ofkernel bandwidth parameters and extends the domain of applicability of low-rankapproximation methods significantly. Our empirical results demonstrate thestability and superiority over the state-of-art kernel approximationalgorithms.

Block Basis Factorization for Scalable Kernel Matrix Evaluation