A Winner-Take-All Autoencoder Based Pieceswise Linear Model for Nonlinear Regression with Missing Data

Huilin Zhu, Yanni Ren, Yanling Tian, Jinglu Hu*

*この研究の対応する著者

研究成果: Article査読

抄録

Missing data is a prevailing problem in predictive analytics. In this paper, a winner-take-all (WTA) autoencoder-based piecewise linear model is developed to solve the nonlinear regression problem under the missing value scenario, which consists of two parts: an overcomplete WTA autoencoder and a gated linear network. The overcomplete WTA autoencoder is a stacked denoising autoencoder (SDAE) designed to play two roles: (1) to estimate the missing values; (2) to realize a sophisticated partitioning by generating a broad set of binary gate control sequences. Besides, an iterative algorithm with renewed teacher signals is developed to train the SDAE. On the other hand, the gated linear network with the generated binary gate control sequences implements a flexible piecewise linear model for nonlinear regression. By composing a quasi-linear kernel based on the gate control sequences, the piecewise linear model is then identified in the same way as a support vector regression. Experimental results have shown that our proposed hybrid model has a better performance than traditional models.

本文言語English
ページ(範囲)1618-1627
ページ数10
ジャーナルIEEJ Transactions on Electrical and Electronic Engineering
16
12
DOI
出版ステータスPublished - 2021 12

ASJC Scopus subject areas

  • 電子工学および電気工学

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