PCA and LDA as dimension reduction for individuality of handwriting in writer verification.
Rimashadira RamleeAzah Kamilah MudaSharifah Sakinah Syed AhmadPublished in: ISDA (2013)
Keyphrases
- dimension reduction
- writer identification
- principal component analysis
- linear discriminant analysis
- principle component analysis
- feature extraction
- dimensionality reduction
- discriminant analysis
- dimension reduction methods
- face recognition
- classical linear discriminant analysis
- principal components analysis
- low dimensional
- random projections
- singular value decomposition
- linear discriminate analysis
- writing styles
- subspace methods
- handwritten documents
- high dimensional
- handwritten words
- manifold learning
- high dimensional data
- discriminative information
- principal components
- small sample size
- support vector
- cluster analysis
- unsupervised learning
- handwriting recognition
- feature subspace
- kernel pca
- scatter matrices
- latent dirichlet allocation
- qr decomposition
- preprocessing
- high dimensionality
- topic models