Dimensionality Reduction using SOM based Technique for Face Recognition.
Dinesh KumarChandra Shekhar RaiShakti KumarPublished in: J. Multim. (2008)
Keyphrases
- dimensionality reduction
- face recognition
- linear discriminant analysis
- dealing with high dimensional data
- principal component analysis
- input space
- neighborhood preserving
- linear dimensionality reduction
- high dimensional
- subspace learning
- self organizing maps
- feature extraction
- unsupervised learning
- discriminant analysis
- sparse representation
- locality preserving projections
- face images
- high dimensionality
- kernel discriminant analysis
- pattern recognition
- low dimensional
- unsupervised feature selection
- feature space
- high dimensional data
- dimensionality reduction methods
- data points
- neural network
- pattern recognition and machine learning
- face databases
- recognition rate
- neighborhood preserving embedding
- data representation
- feature vectors
- computer vision
- random projections
- competitive learning
- kernel pca
- kohonen self organizing map
- lower dimensional
- kohonen self organizing maps
- k means
- human faces
- manifold learning
- local binary pattern
- subspace methods
- input patterns
- structure preserving
- metric learning
- feature selection
- graph embedding
- face recognition algorithms
- pattern classification
- nonlinear dimensionality reduction
- discriminant information
- face recognition systems
- discriminant embedding
- kernel learning
- pose variations