Sharp analysis of low-rank kernel matrix approximations (1208.2015v3)

Published 9 Aug 2012 in cs.LG, math.ST, and stat.TH

Abstract: We consider supervised learning problems within the positive-definite kernel framework, such as kernel ridge regression, kernel logistic regression or the support vector machine. With kernels leading to infinite-dimensional feature spaces, a common practical limiting difficulty is the necessity of computing the kernel matrix, which most frequently leads to algorithms with running time at least quadratic in the number of observations n, i.e., O(n^2). Low-rank approximations of the kernel matrix are often considered as they allow the reduction of running time complexities to O(p² n), where p is the rank of the approximation. The practicality of such methods thus depends on the required rank p. In this paper, we show that in the context of kernel ridge regression, for approximations based on a random subset of columns of the original kernel matrix, the rank p may be chosen to be linear in the degrees of freedom associated with the problem, a quantity which is classically used in the statistical analysis of such methods, and is often seen as the implicit number of parameters of non-parametric estimators. This result enables simple algorithms that have sub-quadratic running time complexity, but provably exhibit the same predictive performance than existing algorithms, for any given problem instance, and not only for worst-case situations.

Authors (1)

Francis Bach (249 papers)

Citations (274)

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Summary

Analysis of Low-Rank Kernel Matrix Approximations

The paper "Sharp analysis of low-rank kernel matrix approximations" by Francis Bach addresses the computation challenges posed by kernel methods in supervised learning, specifically focusing on kernel ridge regression. These methods require forming a kernel matrix with complexities generally at least quadratic in the number of observations, $O(n^2)$ , where $n$ is substantial. The paper investigates the viability of low-rank approximations to reduce these complexities to $O(p^2n)$ , established through approximations of multiple columns of the kernel matrix.

Key Contribution

The pivotal claim in this paper is that in kernel ridge regression, the rank $p$ necessary for these approximations can be aligned linearly with the degrees of freedom inherent in the problem. This paper argues that the degrees of freedom, a statistical aspect typically associated with non-parametric estimators, can be translated into computational insights. Consequently, the paper provides detailed theoretical analyses suggesting that sub-quadratic computation can be achieved without sacrificing predictive accuracy.

Theoretical Insights

This investigation utilizes positive-definite kernel frameworks within supervised learning. The supporting analysis underscores a profound relationship between the degrees of freedom and the requisite rank $p$ for predictive accuracy. The research leverages mathematical tools to demonstrate this interdependence, contrasting traditional bias-variance tradeoffs with a more refined statistical analysis that operates beyond worst-case scenarios.

A pivotal section of the paper discusses the degrees of freedom not merely in the context of statistical risk but also as pertinent bounds for computational resources. The linkage to the regularization parameter $\lambda$ is further scrutinized, revealing that the optimal setting of $\lambda$ depends on the underlying distribution of eigenvalues of the kernel matrix—a valuable insight for the design of efficient kernel-based algorithms.

Algorithmic and Practical Contributions

In terms of practical applications, the paper suggests simple algorithmic procedures that exhibit equivalent predictive performance to traditional algorithms, while maintaining a sub-quadratic complexity. This implies computational efficiency even as the dataset size scales, making these approximations particularly viable for large datasets frequently encountered in real-world applications.

Moreover, the paper critiques and improves on existing related works which focus predominantly on bounding the norm differences between the full and approximated kernel matrices—an approach shown as overly conservative in light of the presented analysis focused on prediction. The proposition here is that sharper bounds could yield more computationally efficient algorithms while maintaining the predictive strengths typically desired in kernel methods.

Implications and Future Directions

The implications of these findings are two-fold: theoretically, they deepen the understanding of the intrinsic relations between statistical and computational aspects of kernel methods; practically, they offer pathways to efficient algorithm design in machine learning applications. Future work could extend these results to other kernels beyond ridge regression, or explore ramifications in settings where the design is random rather than fixed.

The in-depth and non-asymptotic guarantees developed in this work form a potential basis for new advancements in kernel method algorithms, especially in domains requiring large-scale data processing such as genomics and image recognition. Thus, this work forms an essential contribution to the computational efficiency in the landscape of kernel methods, fueling further inquiry into adaptive sampling techniques and their impact on learning models.

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