Heteroscedastic probabilistic linear discriminant analysis for manifold learning in video-based face recognition.
Moh Edi WibowoDian TjondronegoroLigang ZhangIvan HimawanPublished in: WACV (2013)
Keyphrases
- linear discriminant analysis
- manifold learning
- dimensionality reduction
- dimension reduction
- high dimensional data
- feature extraction
- discriminant analysis
- low dimensional
- principal component analysis
- face recognition
- feature space
- high dimensional
- subspace learning
- nonlinear dimensionality reduction
- high dimensionality
- pattern recognition
- principal components analysis
- support vector
- support vector machine svm
- data representation
- subspace methods
- generative model
- dimensionality reduction methods
- input space
- kernel pca
- singular value decomposition
- sparse representation
- unsupervised learning
- manifold structure
- image processing
- euclidean distance
- locally linear embedding
- lower dimensional
- data sets
- embedding space
- metric learning
- euclidean space
- similarity search
- image classification
- data points
- computer vision