kpca

Kernel Principal Components Analysis
[PCV, EIGV, ROTATED] = KPCA(K, TH)
 
 Inputs: 
    (K): Kernel Matrix
    (TH): optional argument with default value 1.e-4. 
        if (th) is a positive integer >= 1 then th PCs are returned,
        elseif (th) \in (0,1) Principal components with eigenvalue lower than (th) are ignored.
 Outputs:
    (PCV): Matrix containing the principal component vectors (stored in columns)
    (EIGV): Vector of eigenvalues corresponding to each eigenvector
    (ROTATED): Projections (rotations) on principal components

Reference:
B. Scholkopf, A. Smola, K.-R. Mueller. Nonlinear component analysis 
as a kernel eigenvalue problem. Neural Computation 10:1299-1319, 1998.