The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space.
Robert JenssenDeniz ErdogmusJosé Carlos PríncipeTorbjørn EltoftPublished in: NIPS (2004)
Keyphrases
- feature space
- cost function
- dissimilarity measure
- kernel function
- data points
- high dimensionality
- kernel methods
- distance metric
- class separability
- probability density function
- high dimensional
- feature selection
- sparse coding
- distance measure
- clustering algorithm
- clustering method
- kernel pca
- input space
- kernel matrix
- generalized gaussian
- distance function
- euclidean distance
- k means
- dimensionality reduction
- low dimensional
- kernel trick
- feature vectors
- similarity function
- kernel space
- affinity measure
- mean shift
- feature extraction
- high dimensional data
- principal component analysis
- dot product
- unsupervised learning
- mixture model
- hyperplane
- support vector machine
- high dimensional feature space
- image retrieval
- uncertain objects
- kernel based nonlinear
- affinity propagation
- density estimation
- spectral clustering
- data clustering
- gaussian mixture model
- probabilistic model