Nonlinear Discriminant Principal Component Analysis for Image Classification and Reconstruction.
Tiene A. FilisbinoGilson A. GiraldiCarlos E. ThomazPublished in: BRACIS (2018)
Keyphrases
- principal component analysis
- image classification
- feature extraction
- kernel pca
- discriminant analysis
- kernel principal component analysis
- dimensionality reduction
- linear dimensionality reduction
- independent component analysis
- dimension reduction
- discriminant subspace
- principal components
- low dimensional
- reconstruction error
- linear discriminant analysis
- face recognition
- bag of words
- feature space
- singular value decomposition
- compressed sensing
- visual words
- image reconstruction
- face images
- image representation
- visual features
- sparse coding
- covariance matrix
- random projections
- reconstruction process
- image processing
- locality preserving projections
- high dimensional feature space
- feature vectors
- high resolution
- reconstruction method
- discrete tomography
- three dimensional
- compressive sensing
- class specific
- negative matrix factorization
- sparse representation
- bag of features
- image features
- high dimensional data
- fisher criterion
- neural network
- feature selection