Parametric PCA for unsupervised metric learning.
Alexandre L. M. LevadaPublished in: Pattern Recognit. Lett. (2020)
Keyphrases
- metric learning
- dimensionality reduction
- semi supervised
- principal component analysis
- unsupervised learning
- feature space
- distance metric learning
- fully supervised
- distance metric
- principal components analysis
- kernel matrix
- pairwise
- learning tasks
- principal components
- supervised learning
- multi task
- semi supervised learning
- distance function
- low dimensional
- kernel pca
- euclidean distance
- feature extraction
- linear discriminant analysis
- dimension reduction
- high dimensional data
- high dimensional
- face recognition
- manifold learning
- dimensionality reduction methods
- pattern recognition
- data sets
- mahalanobis metric
- covariance matrix
- sample size
- unlabeled data
- face images
- labeled data
- image classification
- data points
- k means
- image processing
- machine learning
- neural network