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A0394
Title: Approximate kernel PCA: Computational vs. statistical tradeoff Authors:  Nicholas Sterge - Pennsylvania State University (United States)
Bharath Sriperumbudur - Pennsylvania State University (United States) [presenting]
Abstract: Kernel principal component analysis (KPCA) is a popular non-linear dimensionality reduction technique, which generalizes classical linear PCA by finding functions in a reproducing kernel Hilbert space (RKHS) such that the function evaluation at a random variable $X$ has maximum variance. Despite its popularity, kernel PCA suffers from poor scalability in big data scenarios as it involves solving an $n\times n$ eigensystem leading to the computational complexity of $O(n^3)$ with $n$ being the number of samples. To address this issue we consider random approximations to kernel PCA which requires solving an $m x m$ eigenvalue problem and therefore has a computational complexity of $O(m^3)$, implying that the approximate method is computationally efficient if $m<n$ with $m$ being the number of random features. The goal is to investigate the trade-off between computational and statistical behaviors of approximate KPCA, i.e., whether the computational gain is achieved at the cost of statistical efficiency. We show that the approximate KPCA is both computationally and statistically efficient compared to KPCA in terms of the error associated with reconstructing a kernel function based on its projection onto the corresponding eigenspaces.