How Feature-Induced Manifold Disambiguation Improves Video Tagging in Multi-View Learning

The paper "Feature‑Induced Manifold Disambiguation for Multi‑view Partial Multi‑label Learning" accepted at KDD 2020 introduces the MVPML framework and the FIMAN method, which leverage heterogeneous multimodal features to correct and supplement video tags, thereby boosting distribution efficiency in Alibaba Entertainment’s platforms.

Alibaba EntertainmentKDD 2020manifold disambiguation

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How Feature-Induced Manifold Disambiguation Improves Video Tagging in Multi-View Learning

DataFunTalk

Nov 26, 2019 · Artificial Intelligence

Neural News Recommendation with Attentive Multi‑View Learning and Personalized Attention

This article surveys two neural news recommendation approaches—NAML, which uses multi‑view learning to fuse heterogeneous news information, and NPA, which incorporates personalized attention for both words and news items—demonstrating their superior performance over strong baselines on real‑world MSN news data through extensive experiments and visual analyses.

AIDeep Learningmulti-view learning

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Neural News Recommendation with Attentive Multi‑View Learning and Personalized Attention

Alibaba Cloud Developer

Aug 21, 2019 · Artificial Intelligence

How View-Specific Information Boosts Multi-View Multi-Label Learning (SIMM)

This article explains the SIMM algorithm, a multi‑view multi‑label learning method that extracts view‑specific information alongside shared subspace representations, detailing its motivation, architecture, loss functions, experimental results on eight datasets, and how it outperforms existing approaches.

SIMMadversarial learningmulti-label classification

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How View-Specific Information Boosts Multi-View Multi-Label Learning (SIMM)

Youku Technology

Aug 12, 2019 · Artificial Intelligence

Interpretation of the Paper “Multi-View Multi-Label Learning with View‑Specific Information Extraction” (SIMM)

The article explains SIMM, a neural‑network framework for multi‑view multi‑label learning that jointly extracts a shared, view‑invariant subspace via adversarial loss and orthogonal view‑specific features, demonstrating superior performance across eight benchmark datasets compared to existing MVML and ML‑kNN methods.

AIadversarial learningmachine learning

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Interpretation of the Paper “Multi-View Multi-Label Learning with View‑Specific Information Extraction” (SIMM)