Q-Kernel-Based and Conditionally Negative Definite Kernel-Based Machine Learning Tools
Assing cndkernmatrix class to matrix objects
Assing qkernmatrix class to matrix objects
Block diagonal concatenation of matrix
Class "cndkernel" "nonlkernel" "polykernel" "rbfkernel" "laplkernel"
CND Kernel Functions
CND Kernel Matrix functions
Computes the Euclidean(square Euclidean) distance matrix
Class "qkdbscan"
qKernel-DBSCAN density reachability and connectivity clustering
Class "qkernel" "rbfqkernel" "nonlqkernel" "laplqkernel" "ratiqkernel"
qKernel Functions
qKernel Matrix functions
Class "qkgda"
qKernel Generalized Discriminant Analysis
qKernel Isomap embedding
qKernel Isometric Feature Mapping
Class "qkLLE"
qKernel Locally Linear Embedding
qKernel Metric Multi-Dimensional Scaling
qKernel Metric Multi-Dimensional Scaling
Class "qkpca"
qKernel Principal Components Analysis
Class "qkprc"
Class "qkspecc"
qkernel spectral Clustering
qkernel spectral Clustering
Class "qsammon"
qKernel Sammon Mapping
Class "qtSNE"
qKernel t-Distributed Stochastic Neighbor Embedding
Nonlinear machine learning tool for classification, clustering and dimensionality reduction. It integrates 12 q-kernel functions and 15 conditional negative definite kernel functions and includes the q-kernel and conditional negative definite kernel version of density-based spatial clustering of applications with noise, spectral clustering, generalized discriminant analysis, principal component analysis, multidimensional scaling, locally linear embedding, sammon's mapping and t-Distributed stochastic neighbor embedding.