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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 24 / No. 1: Twenty-Fourth AAAI Conference on Artificial Intelligence

G-Optimal Design with Laplacian Regularization

March 8, 2023

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Authors

Chun Chen

Zhejiang University


Zhengguang Chen

Zhejiang University


Jiajun Bu

Zhejiang University


Can Wang

Zhejiang University


Lijun Zhang

Zhejiang University


Cheng Zhang

China Disabled Persons' Federation Information Center


DOI:

10.1609/aaai.v24i1.7672


Abstract:

In many real world applications, labeled data are usually expensive to get, while there may be a large amount of unlabeled data. To reduce the labeling cost, active learning attempts to discover the most informative data points for labeling. Recently, Optimal Experimental Design (OED) techniques have attracted an increasing amount of attention. OED is concerned with the design of experiments that minimizes variances of a parameterized model. Typical design criteria include D-, A-, and E-optimality. However, all these criteria are based on an ordinary linear regression model which aims to minimize the empirical error whereas the geometrical structure of the data space is not well respected. In this paper, we propose a novel optimal experimental design approach for active learning, called Laplacian G-Optimal Design (LapGOD), which considers both discriminating and geometrical structures. By using Laplacian Regularized Least Squares which incorporates manifold regularization into linear regression, our proposed algorithm selects those data points that minimizes the maximum variance of the predicted values on the data manifold. We also extend our algorithm to nonlinear case by using kernel trick. The experimental results on various image databases have shown that our proposed LapGOD active learning algorithm can significantly enhance the classification accuracy if the selected data points are used as training data.

Topics: AAAI

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HOW TO CITE:

Chun Chen|| Zhengguang Chen|| Jiajun Bu|| Can Wang|| Lijun Zhang|| Cheng Zhang G-Optimal Design with Laplacian Regularization Proceedings of the AAAI Conference on Artificial Intelligence, 24 (2010) 413.

Chun Chen|| Zhengguang Chen|| Jiajun Bu|| Can Wang|| Lijun Zhang|| Cheng Zhang G-Optimal Design with Laplacian Regularization AAAI 2010, 413.

Chun Chen|| Zhengguang Chen|| Jiajun Bu|| Can Wang|| Lijun Zhang|| Cheng Zhang (2010). G-Optimal Design with Laplacian Regularization. Proceedings of the AAAI Conference on Artificial Intelligence, 24, 413.

Chun Chen|| Zhengguang Chen|| Jiajun Bu|| Can Wang|| Lijun Zhang|| Cheng Zhang. G-Optimal Design with Laplacian Regularization. Proceedings of the AAAI Conference on Artificial Intelligence, 24 2010 p.413.

Chun Chen|| Zhengguang Chen|| Jiajun Bu|| Can Wang|| Lijun Zhang|| Cheng Zhang. 2010. G-Optimal Design with Laplacian Regularization. "Proceedings of the AAAI Conference on Artificial Intelligence, 24". 413.

Chun Chen|| Zhengguang Chen|| Jiajun Bu|| Can Wang|| Lijun Zhang|| Cheng Zhang. (2010) "G-Optimal Design with Laplacian Regularization", Proceedings of the AAAI Conference on Artificial Intelligence, 24, p.413

Chun Chen|| Zhengguang Chen|| Jiajun Bu|| Can Wang|| Lijun Zhang|| Cheng Zhang, "G-Optimal Design with Laplacian Regularization", AAAI, p.413, 2010.

Chun Chen|| Zhengguang Chen|| Jiajun Bu|| Can Wang|| Lijun Zhang|| Cheng Zhang. "G-Optimal Design with Laplacian Regularization". Proceedings of the AAAI Conference on Artificial Intelligence, 24, 2010, p.413.

Chun Chen|| Zhengguang Chen|| Jiajun Bu|| Can Wang|| Lijun Zhang|| Cheng Zhang. "G-Optimal Design with Laplacian Regularization". Proceedings of the AAAI Conference on Artificial Intelligence, 24, (2010): 413.

Chun Chen|| Zhengguang Chen|| Jiajun Bu|| Can Wang|| Lijun Zhang|| Cheng Zhang. G-Optimal Design with Laplacian Regularization. AAAI[Internet]. 2010[cited 2023]; 413.


ISSN: 2374-3468


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