Pareto Optimization of Parameter Selection Speeds Up and Improves Quality of Motion Computation: Applying Evolutionary Multi-objective Optimization to Randomized-Subspace Robust PCA.
David GrobMehmet VurkacAgnieszka MiguelMirka MandichRana BayrakcsmithPublished in: ICDM Workshops (2018)
Keyphrases
- parameter selection
- evolutionary multi objective optimization
- principal component analysis
- multi objective
- optimization algorithm
- adaptive regularization
- principal components analysis
- low dimensional
- high dimensional
- dimensionality reduction
- principal components
- high quality
- feature space
- motion estimation
- feature extraction
- linear subspace
- kernel ridge regression
- subspace methods
- genetic algorithm
- optical flow
- high dimensional data
- face recognition
- subspace learning
- image sequences
- model selection
- least squares
- maximum likelihood
- camera motion
- dimension reduction
- training data
- clustering algorithm
- computer vision
- neural network