Large Datasets
In practice, a large data set leads to a large K, and storing K may become a problem. One way to deal with this is to perform clustering on your large dataset, and populate the kernel with the means of those clusters. Since even this method may yield a relatively large K, it is common to compute only the top P eigenvalues and eigenvectors of K.
Read more about this topic: Kernel Principal Component Analysis
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