In the field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space. Background: Linear PCA Recall that conventional PCA operates on zero-centered data; that is, , where is one of the multivariate observations. It operates by diagonalizing the covariance matrix, in other words, it gives an eigendecomposition of the covariance matrix: which can be rewritten as . (See also: Covariance matrix as a linear operator) Introduction of the Kernel to PCA To understand the utility of kernel PCA, particularly for clustering, observe that, while N points cannot, in general, be linearly separated in dimensions, they can almost always be linearly separated in dimensions. That is, given N points, , if we map them to an N-dimensional space with where , it is easy to construct a hyperplane that divides the points into arbitrary clusters. Of course, this creates linearly independent vectors, so there is no covariance on which to perform eigendecomposition explicitly as we would in linear PCA. Instead, in kernel PCA, a non-trivial, arbitrary function is 'chosen' that is never calculated explicitly, allowing the possibility to use very-high-dimensional 's if we never have to actually evaluate the data in that space. Since we generally try to avoid working in the -space, which we will call the 'feature space', we can create the N-by-N kernel which represents the inner product space (see Gramian matrix) of the otherwise intractable feature space. The dual form that arises in the creation of a kernel allows us to mathematically formulate a version of PCA in which we never actually solve the eigenvectors and eigenvalues of the covariance matrix in the -space (see Kernel trick). The N-elements in each column of K represent the dot product of one point of the transformed data with respect to all the transformed points (N points). Some well-known kernels are shown in the example below. Because we are never working directly in the feature space, the kernel-formulation of PCA is restricted in that it computes not the principal components themselves, but the projections of our data onto those components. To evaluate the projection from a point in the feature space onto the kth principal component (where superscript k means the component k, not powers of k) We note that