SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality Reduction.
Leland BarnardFarwa AliHugo BothaDavid T. JonesPublished in: CoRR (2023)
Keyphrases
- dimensionality reduction
- feature extraction
- high dimensional
- random projections
- sparse representation
- principal component analysis
- manifold learning
- low dimensional
- high dimensional data
- linear discriminant analysis
- feature space
- feature selection
- high dimensionality
- pattern recognition
- data representation
- sparse data
- face recognition
- image processing
- dimension reduction
- compressive sensing
- discriminant projection
- feature vectors
- image classification
- discriminant analysis
- data structure
- sparse coding
- preprocessing
- frequency domain
- pattern classification
- dimensionality reduction methods
- lower dimensional
- support vector machine svm
- pattern recognition and machine learning
- compressed domain
- feature set
- preprocessing step
- wavelet transform
- kernel pca
- locally linear embedding
- data compression
- input space
- iris recognition
- metric learning
- data points
- nearest neighbor
- feature representation
- nonlinear dimensionality reduction
- compressed data
- graph embedding
- unsupervised feature selection
- linear dimensionality reduction
- principal components
- extracted features