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

In many statistical modeling problems, such as classification and regression,it is common to encounter sparse and blocky coefficients. Sparse fused Lasso isspecifically designed to recover these sparse and blocky structured features,especially in cases where the design matrix has ultrahigh dimensions, meaningthat the number of features significantly surpasses the number of samples.Quantile loss is a well-known robust loss function that is widely used instatistical modeling. In this paper, we propose a new sparse fused lassoclassification model, and develop a unified multi-block linearized alternatingdirection method of multipliers algorithm that effectively selects sparse andblocky features for regression and classification. Our algorithm has beenproven to converge with a derived linear convergence rate. Additionally, ouralgorithm has a significant advantage over existing methods for solvingultrahigh dimensional sparse fused Lasso regression and classification modelsdue to its lower time complexity. Note that the algorithm can be easilyextended to solve various existing fused Lasso models. Finally, we presentnumerical results for several synthetic and real-world examples, whichdemonstrate the robustness, scalability, and accuracy of the proposedclassification model and algorithm

Multi-block linearized alternating direction method for sparse fused Lasso modeling problems