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

One of the most important problems in regression-based error model ismodeling the complex representation error caused by various corruptions andenvironment changes in images. For example, in robust face recognition, imagesare often affected by varying types and levels of corruptions, such as randompixel corruptions, block occlusions, or disguises. However, existing works arenot robust enough to solve this problem due to they cannot model the complexcorrupted errors very well. In this paper, we address this problem by a unifiedsparse weight learning and low-rank approximation regression model, whichenables the random noises and contiguous occlusions in images to be treatedsimultaneously. For the random noise, we define a generalized correntropy (GC)function to match the error distribution. For the structured error caused byocclusions or disguises, we propose a GC function based rank approximation tomeasure the rank of error matrices. Since the proposed objective function isnon-convex, an effective iterative optimization algorithm is developed toachieve the optimal weight learning and low-rank approximation. Extensiveexperimental results on three public face databases show that the proposedmodel can fit the error distribution and structure very well, thus obtainbetter recognition accuracies in comparison with the existing methods.

A Unified Weight Learning and Low-Rank Regression Model for Robust Complex Error Modeling