Incremental learning algorithm for large-scale semi-supervised ordinal regression

Haiyan Chen

doi:10.1016/j.neunet.2022.02.004

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Incremental learning algorithm for large-scale semi-supervised ordinal regression

2022-05-01

PRE

DOI：10.1016/j.neunet.2022.02.004

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Abstract

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As a special case of multi-classification, ordinal regression (also known as ordinal classification) is a popular method to tackle the multi-class problems with samples marked by a set of ranks. Semi supervised ordinal regression (SSOR) is especially important for data mining applications because semi-supervised learning can make use of the unlabeled samples to train a high-quality learning model. However, the training of large-scale SSOR is still an open question due to its complicated formulations and non-convexity to the best of our knowledge. To address this challenging problem, in this paper, we propose an incremental learning algorithm for SSOR (IL-SSOR), which can directly update the solution of SSOR based on the KKT conditions. More critically, we analyze the finite convergence of IL-SSOR which guarantees that SSOR can converge to a local minimum based on the framework of concave-convex procedure. To the best of our knowledge, the proposed new algorithm is the first efficient on-line learning algorithm for SSOR with local minimum convergence guarantee. The experimental results show, IL-SSOR can achieve better generalization than other semi-supervised multi-class algorithms. Compared with other semi-supervised ordinal regression algorithms, our experimental results show that IL-SSOR can achieve similar generalization with less running time. (C) 2022 Elsevier Ltd. All rights reserved.

Keywords:

Semi-supervised ordinal regression

Incremental learning

Concave-Convex procedure algorithm

Path following algorithm