Parameterized principal component analysis
作者：
Highlights：
• A method for manifold approximation where the low dimensional space is a PCA model with the mean and principal vectors modeled as smooth functions of a parameter that depends on the position on the manifold.
• Generalizations where the manifold dimension is not constant.
• Generalization where the dimensionality of the ambient space is not constant.
• Comparison with PCA, Sparse PCA, and independent PCA models across the manifold, for simulated data, faces in the presence of in plane rotation and faces with different out of plane rotations.
摘要
•A method for manifold approximation where the low dimensional space is a PCA model with the mean and principal vectors modeled as smooth functions of a parameter that depends on the position on the manifold.•Generalizations where the manifold dimension is not constant.•Generalization where the dimensionality of the ambient space is not constant.•Comparison with PCA, Sparse PCA, and independent PCA models across the manifold, for simulated data, faces in the presence of in plane rotation and faces with different out of plane rotations.
论文关键词：Manifold learning,Manifold approximation,Face modeling,Principal component analysis
论文评审过程：Received 16 September 2016, Revised 30 November 2017, Accepted 22 January 2018, Available online 31 January 2018, Version of Record 3 February 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.01.018
