On Orthogonal Projections for Dimension Reduction and Applications in Augmented Target Loss Functions for Learning Problems.
Anna BregerJosé Ignacio OrlandoPavol HarárMonika DörflerSophie KlimschaChristoph GrechenigBianca S. GerendasUrsula Schmidt-ErfurthMartin EhlerPublished in: J. Math. Imaging Vis. (2020)
Keyphrases
- dimension reduction
- learning problems
- loss function
- kernel methods
- learning models
- learning tasks
- reproducing kernel hilbert space
- supervised learning
- learning algorithm
- machine learning algorithms
- pairwise
- support vector
- unsupervised learning
- feature extraction
- feature selection
- principal component analysis
- feature space
- binary classification
- machine learning
- high dimensional
- random projections
- semi supervised learning
- high dimensional data
- cluster analysis
- dimensionality reduction
- low dimensional
- singular value decomposition
- reinforcement learning
- linear discriminant analysis
- data mining
- multi task
- neural network
- high dimensionality
- image processing
- object recognition
- labeled data
- maximum likelihood
- multiple kernel learning
- collaborative filtering