Efficient classification with sparsity augmented collaborative representation
作者：
Highlights：
• Sparsity of collaborative representation explicitly contributes to classification.
• Computational gain is achievable in classification without ignoring sparsity.
• Combining dense and sparse representations is better than using them alone.
• Efficient classification scheme is proposed based on combined representation.
• The scheme out-performs the existing techniques both in accuracy and efficiency.
摘要
Highlights•Sparsity of collaborative representation explicitly contributes to classification.•Computational gain is achievable in classification without ignoring sparsity.•Combining dense and sparse representations is better than using them alone.•Efficient classification scheme is proposed based on combined representation.•The scheme out-performs the existing techniques both in accuracy and efficiency.
论文关键词：Multi-class classification,Sparse representation,Collaborative representation
论文评审过程：Received 24 May 2016, Revised 14 December 2016, Accepted 15 December 2016, Available online 21 December 2016, Version of Record 28 December 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.12.017
