Abstract
In order to overcome the drawbacks of the ridge regression and label propagation algorithms, we propose a new semi-supervised classification method named semi-supervised ridge regression with adaptive graph-based label propagation (SSRR-AGLP). Firstly, we present a new adaptive graph-learning scheme and integrate it into the procedure of label propagation, in which the locality and sparsity of samples are considered simultaneously. Then, we introduce the ridge regression algorithm into label propagation to solve the “out of sample” problem. As a consequence, the proposed SSSRR-AGLP integrates adaptive graph learning, label propagation and ridge regression into a unified framework. Finally, an effective iterative updating algorithm is designed for solving the algorithm, and the convergence analysis is also provided. Extensive experiments are conducted on five databases. Through comparing the results with some well-known algorithms, the effectiveness and superiority of the proposed algorithm are demonstrated.
Highlights
Least square regression (LSR) is a mathematical optimization algorithm that seeks the best matching function of data by minimizing the square of error [1,2,3,4]
In order to improve the performance of retargeted least squares regression (ReLSR), Wang et al [8] proposed the groupwise retargeted least squares regression (GReLSR) algorithm, which utilized an additional regularization to restrict the translation values of ReLSR so that similar values are within the same class
From Equation (18), it clearly can be seen that the proposed model combines the adaptive graph, label propagation algorithm and ridge regression algorithm together to solve the following problems: (a) By introducing the LP algorithm into our method, it can solve the problem that the traditional
Summary
Least square regression (LSR) is a mathematical optimization algorithm that seeks the best matching function of data by minimizing the square of error [1,2,3,4]. It is necessary to utilize the information of unlabeled samples to improve the performance of ridge regression algorithm [39,40]. To reduce the distributed error and enlarge the number of data subsets using unlabeled data, Chang et al [40] provided an error analysis for distributed semi-supervised learning with kernel ridge regression (DSKRR) based on a divide-and-conquer strategy. These semi-supervised ridge regression algorithms utilized the labeled and unlabeled data simultaneously, the distribution relationships between the labeled and unlabeled data were not considered at all.
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