Embracing sparse features: Partial Multi-Label Learning via noisy label identification

Xiaoying Wu

doi:10.1016/j.patcog.2026.113756

Embracing sparse features: Partial Multi-Label Learning via noisy label identification

2026-04-22

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DOI：10.1016/j.patcog.2026.113756

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Abstract

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• A framework for multi-label learning with sparse feature representations. • The algorithm jointly recovers ground-truth structures from features and labels. • The PML-SD algorithm exhibits strong scalability and low time complexity.

Keywords:

multi-label learning

sparse features

label identification

feature recovery

scalability

Embracing sparse features: Partial Multi-Label Learning via noisy label identification

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