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/

Zendy Plus

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

Zendy Tools

Zendy Open

Feature selection is important in many big data applications. Two criticalchallenges closely associate with big data. Firstly, in many big dataapplications, the dimensionality is extremely high, in millions, and keepsgrowing. Secondly, big data applications call for highly scalable featureselection algorithms in an online manner such that each feature can beprocessed in a sequential scan. We present SAOLA, a Scalable and AccurateOnLine Approach for feature selection in this paper. With a theoreticalanalysis on bounds of the pairwise correlations between features, SAOLA employsnovel pairwise comparison techniques and maintain a parsimonious model overtime in an online manner. Furthermore, to deal with upcoming features thatarrive by groups, we extend the SAOLA algorithm, and then propose a newgroup-SAOLA algorithm for online group feature selection. The group-SAOLAalgorithm can online maintain a set of feature groups that is sparse at thelevels of both groups and individual features simultaneously. An empiricalstudy using a series of benchmark real data sets shows that our two algorithms,SAOLA and group-SAOLA, are scalable on data sets of extremely highdimensionality, and have superior performance over the state-of-the-art featureselection methods.

Scalable and Accurate Online Feature Selection for Big Data