Soft-Constrained Nonparametric Density Estimation with Artificial Neural Networks
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- 关键词：Density estimation ; Nonparametric estimation ; Unsupervised learning ; Constrained learning ; Multilayer perceptron
- 刊名：Lecture Notes in Computer Science
- 出版年：2016
- 出版时间：2016
- 年：2016
- 卷：9896
- 期：1
- 页码：68-79
- 全文大小：746 KB
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17. Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università degli Studi di Siena, Siena, Italy - 丛书名：Artificial Neural Networks in Pattern Recognition
- ISBN：978-3-319-46182-3
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
- 卷排序：9896
文摘
The estimation of probability density functions (pdf) from unlabeled data samples is a relevant (and, still open) issue in pattern recognition and machine learning. Statistical parametric and nonparametric approaches present severe drawbacks. Only a few instances of neural networks for pdf estimation are found in the literature, due to the intrinsic difficulty of unsupervised learning under the necessary integral-equals-one constraint. In turn, also such neural networks do suffer from serious limitations. The paper introduces a soft-constrained algorithm for training a multilayer perceptron (MLP) to estimate pdfs empirically. A variant of the Metropolis-Hastings algorithm (exploiting the very probabilistic nature of the MLP) is used to satisfy numerically the constraint on the integral of the function learned by the MLP. The preliminary outcomes of a simulation on data drawn from a mixture of Fisher-Tippett pdfs are reported on, and compared graphically with the estimates yielded by statistical techniques, showing the viability of the approach.