Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression.
Biprateep DeyJeffrey A. NewmanBrett H. AndrewsRafael IzbickiAnn B. LeeDavid ZhaoMarkus Michael RauAlex I. MalzPublished in: CoRR (2021)
Keyphrases
- probability distribution
- feature space
- mercer kernels
- regression model
- high dimensional
- support vector regression
- mean shift
- kernel function
- feature vectors
- gaussian kernels
- random variables
- simple linear
- high dimensionality
- bayesian networks
- feature selection
- hyperplane
- training samples
- data points
- principal component analysis
- regression method
- feature extraction
- classification accuracy
- dimensionality reduction
- gaussian processes
- neural network
- regression algorithm
- high dimensional feature space
- model selection
- dissimilarity measure
- input space
- low dimensional
- support vector
- training set
- image retrieval
- computer vision
- image processing
- input data
- support vector machine
- image representation
- dot product
- locally weighted
- regression function
- data sets
- canonical correlation analysis
- riemannian manifolds
- kernel matrix
- regression analysis
- photometric stereo
- multiscale