ar X iv : 0 90 2 . 33 47 v 1 [ st at . M L ] 1 9 Fe b 20 09 Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression ∗
نویسندگان
چکیده
The runtime for Kernel Partial Least Squares (KPLS) to compute the fit is quadratic in the number of examples. However, the necessity of obtaining sensitivity measures as degrees of freedom for model selection or confidence intervals for more detailed analysis requires cubic runtime, and thus constitutes a computational bottleneck in real-world data analysis. We propose a novel algorithm for KPLS which not only computes (a) the fit, but also (b) its approximate degrees of freedom and (c) error bars in quadratic runtime. The algorithm exploits a close connection between Kernel PLS and the Lanczos algorithm for approximating the eigenvalues of symmetric matrices, and uses this approximation to compute the trace of powers of the kernel matrix in quadratic runtime.
منابع مشابه
Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression
The Degrees of Freedom of Kernel Partial Least Squares (KPLS) require all eigenvalues of the kernel matrix K, hence the computation is cubic in the number of observations n. •We use Kernel PLS itself to approximate the eigenvalues of the kernel matrix. −→ We can compute approximate Degrees of Freedom of KPS in O ( n2 ) ! •We can also compute approximate confidence intervals for KPLS in O ( n2 ) !
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