Schrödinger PCA: You Only Need Variances for Eigenmodes.
Ziming LiuSitian QianYixuan WangYuxuan YanTianyi YangPublished in: CoRR (2020)
Keyphrases
- hamilton jacobi
- heat equation
- distance transform
- principal component analysis
- wave equation
- eikonal equation
- principal components analysis
- principal components
- finite difference
- medial axis
- dimensionality reduction
- steady state
- principle component analysis
- scale space
- partial differential equations
- feature extraction
- appearance based object recognition
- face recognition
- covariance matrix
- independent component analysis
- diffusion process
- scale spaces
- linear discriminant analysis
- gaussian kernel
- face images
- dimension reduction
- level set method
- spherical harmonics
- kernel pca
- feature space
- high dimensional
- support vector machine
- image segmentation
- image brightness
- kernel principal component analysis
- subspace methods
- negative matrix factorization
- endpoints