kpca_predict

Returns projections of feature vectors giving rise to kernel matrix (Knew) onto principal components vectors (pcv) of the kernel matrix (K)
PREDICT = KPCA_PREDICT(K,KNEW,PCV)

 Inputs:
    (K): Kernel matrix on which principal component vectors (PCV) were estimated through KPCA function
    (KNEW): Kernel matrix of inner products (in feature space) between observations that 
        are to be predicted and observations that gave rise to (K)
    (PCV): Principal components vectors of (K) as estimated through KPCA

 Output:
    (PREDICT): Projection of feature vectors in (KNEW) onto (PCV)

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