Multi-view Sparse Laplacian Eigenmaps for nonlinear Spectral Feature Selection.
Gaurav SrivastavaMahesh JangidPublished in: ICSSE (2023)
Keyphrases
- multi view
- laplacian eigenmaps
- kernel pca
- feature selection
- dimensionality reduction
- feature space
- manifold learning
- high dimensional
- feature extraction
- multiple views
- single view
- kernel methods
- d objects
- sparse representation
- kernel function
- principal component analysis
- semi supervised
- support vector machine
- three dimensional
- high dimensionality
- spectral analysis
- support vector
- graph laplacian
- spectral clustering
- feature set
- low dimensional
- locally linear embedding
- high dimensional data
- range images
- kernel matrix
- dimension reduction
- machine learning
- visual hull
- computer vision
- surface reconstruction
- model selection
- text classification
- face recognition
- euclidean space
- co training
- unsupervised learning
- classification accuracy
- training set
- training data