With the massive volume and rapid increasing of data, feature space study is of great importance. To avoid the complex training processes in deep learning models which project original feature space into low-dimensional ones, we propose a novel feature space learning (FSL) model. The main contributions in our approach are: (1) FSL can not only select useful features but also adaptively update feature values and span new feature spaces; (2) four FSL algorithms are proposed with the feature space updating procedure; (3) FSL can provide a better data understanding and learn descriptive and compact feature spaces without the tough training for deep architectures. Experimental results on benchmark data sets demonstrate that FSL-based algorithms performed better than the classical unsupervised, semi-supervised learning and even incremental semi-supervised algorithms. In addition, we show a visualization of the learned feature space results. With the carefully designed learning strategy, FSL dynamically disentangles explanatory factors, depresses the noise accumulation and semantic shift, and constructs easy-to-understand feature spaces.

Feature space learning model / Guan, Renchu; Wang, Xu; Marchese, Maurizio; Yang, Mary Qu; Liang, Yanchun; Yang, Chen. - In: JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING. - ISSN 1868-5137. - ELETTRONICO. - 2019, 10:5(2019), pp. 1-12. [10.1007/s12652-018-0805-4]

Feature space learning model

Marchese, Maurizio;Liang, Yanchun;
2019

Abstract

With the massive volume and rapid increasing of data, feature space study is of great importance. To avoid the complex training processes in deep learning models which project original feature space into low-dimensional ones, we propose a novel feature space learning (FSL) model. The main contributions in our approach are: (1) FSL can not only select useful features but also adaptively update feature values and span new feature spaces; (2) four FSL algorithms are proposed with the feature space updating procedure; (3) FSL can provide a better data understanding and learn descriptive and compact feature spaces without the tough training for deep architectures. Experimental results on benchmark data sets demonstrate that FSL-based algorithms performed better than the classical unsupervised, semi-supervised learning and even incremental semi-supervised algorithms. In addition, we show a visualization of the learned feature space results. With the carefully designed learning strategy, FSL dynamically disentangles explanatory factors, depresses the noise accumulation and semantic shift, and constructs easy-to-understand feature spaces.
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Guan, Renchu; Wang, Xu; Marchese, Maurizio; Yang, Mary Qu; Liang, Yanchun; Yang, Chen
Feature space learning model / Guan, Renchu; Wang, Xu; Marchese, Maurizio; Yang, Mary Qu; Liang, Yanchun; Yang, Chen. - In: JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING. - ISSN 1868-5137. - ELETTRONICO. - 2019, 10:5(2019), pp. 1-12. [10.1007/s12652-018-0805-4]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/226810
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