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

Traditional multi-view feature extraction methods based on manifold learning frequently overlook the similarity sequence between samples, failing to capture the intrinsic manifold structure of raw nonlinear samples and restricting the recognition performance of multi-view learning. In this paper, we propose a novel similarity-sequenced multi-view discriminant feature extraction method, called Similarity -sequenced Multi-view Discriminant Correlation Analysis (SMDCA), which explicitly considers the sample sequences based on similarity. The method constructs similarity-sequenced discriminant scatters for preserving the sequence structure of within-class samples and develops between-class correlations with the similarity-sequence structure information for further constraining intrinsic manifold structure of cross-view samples. SMDCA can also simultaneously extract low-dimensional sequence features with well-discriminative power from multiple views. Extensive experiments exhibit that SMDCA can provide higher recognition accuracy and stronger robustness in image recognition tasks.

Similarity-sequenced multi-view discriminant feature extraction for image recognition