Feature space learning model

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.

A step towards the live identification of pipe obstructions with the use of passive acoustic emission and supervised machine learning

“…The number of attributes n are directly related to the class observed from experience or the experiments. In order to achieve a better classification between data classes, each class will be plotted in another dimensional space, the so-called feature space (Guan et al, 2019;Masud et al, 2010). Figure 2 1 illustrates the presentation of machine learning data.…”