Using an ICA Representation of High Dimensional Data for Object Recognition and Classification.
Marco BressanDavid GuillametJordi VitriàPublished in: CVPR (1) (2001)
Keyphrases
- high dimensional data
- high dimensionality
- dimension reduction
- object recognition
- dimensionality reduction
- low dimensional
- nearest neighbor
- feature extraction
- high dimensional
- regression problems
- data sets
- small sample size
- pattern recognition
- subspace clustering
- original data
- high dimensions
- independent component analysis
- data analysis
- high dimensional feature spaces
- image representation
- similarity search
- data points
- clustering high dimensional data
- manifold learning
- linear discriminant analysis
- support vector machine
- high dimensional spaces
- image classification
- preprocessing
- support vector
- input space
- neural network
- class labels
- support vector machine svm
- machine learning
- computer vision
- feature selection
- principal component analysis
- training set
- variable weighting
- nonlinear dimensionality reduction
- face recognition
- subspace learning
- lower dimensional
- text data
- signal processing
- sparse representation
- euclidean distance
- feature space
- latent space
- input data
- text classification
- multivariate temporal data