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

Manifold learning covers those learning algorithms where high-dimensional data is assumed to lie on a lowdimensional manifold (usually nonlinear). Specific classification algorithms are able to preserve this manifold structure. On the other hand, ordinal regression covers those learning problems where the objective is to classify patterns into labels from a set of ordered categories. There have been very few works combining both ordinal regression and manifold learning. Additionally, privileged information refers to some special features which are available during classifier training, but not in the test phase. This paper contributes a new algorithm for combining ordinal regression and manifold learning, based on the idea of constructing a neighbourhood graph and obtaining the shortest path between all pairs of patterns. Moreover, we propose to exploit privileged information during graph construction, in order to obtain a better representation of the underlying manifold. The approach is tested with one synthetic experiment and 5 real ordinal datasets, showing a promising potential.

Incorporating Privileged Information to Improve Manifold Ordinal Regression