A comparison of PCA, KPCA and LDA for feature extraction to recognize affect in gait kinematics.
Michelle KargRobert JenkeWolfgang SeiberlK. KuuhnlenzA. SchwirtzMartin BussPublished in: ACII (2009)
Keyphrases
- linear discriminant analysis
- feature extraction
- kernel principal component analysis
- principal component analysis
- face recognition
- discriminant analysis
- principal components analysis
- dimensionality reduction
- kernel pca
- gait recognition
- subspace methods
- feature space
- pca lda
- dimension reduction
- principal components
- support vector machine svm
- human identification
- gabor features
- small sample size
- preprocessing
- principle component analysis
- face images
- feature vectors
- pattern classification
- support vector
- kernel function
- high dimensional
- gabor wavelets
- singular value decomposition
- feature selection
- recognition rate
- kernel methods
- covariance matrix
- high dimensional data
- fisher criterion
- low dimensional
- null space
- discriminant projection
- scatter matrices
- extracting features
- linear discriminate analysis
- human recognition
- human gait
- high dimensional feature space
- independent component analysis
- gabor filters