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

The big data trend has inspired feature-driven learning tasks, which cannotbe handled by conventional machine learning models. Unstructured data producesvery large binary matrices with millions of columns when converted to vectorform. However, such data is often sparse, and hence can be manageable throughthe use of sparse random projections.  This work studies efficient non-iterative and iterative methods suitable forsuch data, evaluating the results on two representative machine learning taskswith millions of samples and features. An efficient Jaccard kernel isintroduced as an alternative to the sparse random projection. Findings indicatethat non-iterative methods can find larger, more accurate models than iterativemethods in different application scenarios.

Comparison of Classification Methods for Very High-Dimensional Data in Sparse Random Projection Representation