Semi-supervised local multi-manifold Isomap by linear embedding for feature extraction
作者：
Highlights：
• We explore the discriminative feature extraction problem.
• A Semi‐Supervised local multi‐manifold Isomap by linear embedding is proposed.
• Our model can use labeled and unlabeled data to deliver manifold features.
• Our model aims to minimize pairwise intra‐class distances in the same manifold.
• Our model aims to maximize the distances between different manifolds.
摘要
•We explore the discriminative feature extraction problem.•A Semi‐Supervised local multi‐manifold Isomap by linear embedding is proposed.•Our model can use labeled and unlabeled data to deliver manifold features.•Our model aims to minimize pairwise intra‐class distances in the same manifold.•Our model aims to maximize the distances between different manifolds.
论文关键词：Semi-supervised manifold feature extraction,Local multi-manifold Isomap,Linear embedding,Classification
论文评审过程：Received 1 October 2016, Revised 29 September 2017, Accepted 30 September 2017, Available online 13 October 2017, Version of Record 8 January 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.09.043
