Scalable Kernel Ordinal Regression via Doubly Stochastic Gradients

Ordinal regression (OR) is one of the most important machine learning tasks. The kernel method is a major technique to achieve nonlinear OR. However, traditional kernel OR solvers are inefficient due to increased complexity introduced by multiple ordinal thresholds as well as the cost of kernel comp...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 32; číslo 8; s. 3677 - 3689
Hlavní autori: Gu, Bin, Geng, Xiang, Li, Xiang, Shi, Wanli, Zheng, Guansheng, Deng, Cheng, Huang, Heng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Approximation algorithms
Cognitive tasks
Convergence
Doubly stochastic gradients (DSGs)
Hilbert space
Iterative methods
Kernel
kernel learning
Kernels
Learning algorithms
Machine learning
Memory management
Optimization
ordinal regression (OR)
random features
Solvers
Stochastic processes
Stochasticity
Support vector machines
Thresholds
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:Ordinal regression (OR) is one of the most important machine learning tasks. The kernel method is a major technique to achieve nonlinear OR. However, traditional kernel OR solvers are inefficient due to increased complexity introduced by multiple ordinal thresholds as well as the cost of kernel computation. Doubly stochastic gradient (DSG) is a very efficient and scalable kernel learning algorithm that combines random feature approximation with stochastic functional optimization. However, the theory and algorithm of DSG can only support optimization tasks within the unique reproducing kernel Hilbert space (RKHS), which is not suitable for OR problems where the multiple ordinal thresholds usually lead to multiple RKHSs. To address this problem, we construct a kernel whose RKHS can contain the decision function with multiple thresholds. Based on this new kernel, we further propose a novel DSG-like algorithm, DSGOR. In each iteration of DSGOR, we update the decision functional as well as the function bias with appropriately set learning rates for each. Our theoretic analysis shows that DSGOR can achieve <inline-formula> <tex-math notation="LaTeX">O(1/t) </tex-math></inline-formula> convergence rate, which is as good as DSG, even though dealing with a much harder problem. Extensive experimental results demonstrate that our algorithm is much more efficient than traditional kernel OR solvers, especially on large-scale problems.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.3015937