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

Scientific observations may consist of a large number of variables(features). Identifying a subset of meaningful features is often ignored inunsupervised learning, despite its potential for unraveling clear patternshidden in the ambient space. In this paper, we present a method forunsupervised feature selection, and we demonstrate its use for the task ofclustering. We propose a differentiable loss function that combines theLaplacian score, which favors low-frequency features, with a gating mechanismfor feature selection. We improve the Laplacian score, by replacing it with agated variant computed on a subset of features. This subset is obtained using acontinuous approximation of Bernoulli variables whose parameters are trained togate the full feature space. We mathematically motivate the proposed approachand demonstrate that in the high noise regime, it is crucial to compute theLaplacian on the gated inputs, rather than on the full feature set.Experimental demonstration of the efficacy of the proposed approach and itsadvantage over current baselines is provided using several real-world examples.

Differentiable Unsupervised Feature Selection based on a Gated Laplacian