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

Kernel-based methods enjoy powerful generalization capabilities in handling avariety of learning tasks. When such methods are provided with sufficienttraining data, broadly-applicable classes of nonlinear functions can beapproximated with desired accuracy. Nevertheless, inherent to the nonparametricnature of kernel-based estimators are computational and memory requirementsthat become prohibitive with large-scale datasets. In response to thisformidable challenge, the present work puts forward a low-rank, kernel-based,feature extraction approach that is particularly tailored for online operation,where data streams need not be stored in memory. A novel generative model isintroduced to approximate high-dimensional (possibly infinite) features via alow-rank nonlinear subspace, the learning of which leads to a direct kernelfunction approximation. Offline and online solvers are developed for thesubspace learning task, along with affordable versions, in which the number ofstored data vectors is confined to a predefined budget. Analytical resultsprovide performance bounds on how well the kernel matrix as well askernel-based classification and regression tasks can be approximated byleveraging budgeted online subspace learning and feature extraction schemes.Tests on synthetic and real datasets demonstrate and benchmark the efficiencyof the proposed method when linear classification and regression is applied tothe extracted features.

Large-scale Kernel-based Feature Extraction via Budgeted Nonlinear Subspace Tracking