Sparse graphs with smoothness constraints: Application to dimensionality reduction and semi-supervised classification
作者：
Highlights：
• A constrained sparse graph construction method is proposed.
• The method does not require a predefined affinity matrix.
• The proposed constraints impose edge weights smoothness.
• The proposed constraints lead to a structured sparse graph.
• Performance is assessed on graph-based label propagation and embedding.
摘要
•A constrained sparse graph construction method is proposed.•The method does not require a predefined affinity matrix.•The proposed constraints impose edge weights smoothness.•The proposed constraints lead to a structured sparse graph.•Performance is assessed on graph-based label propagation and embedding.
论文关键词：Graph construction,Sparse representation,Manifold constraints,Laplacian smoothness,Graph-based semi-supervised learning,Label propagation,Graph-based embedding,Classification,
论文评审过程：Received 22 November 2018, Revised 14 April 2019, Accepted 24 June 2019, Available online 29 June 2019, Version of Record 5 July 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.06.015
