Yuna Jeong  Myunggwon Hwang  Wonkyung Sung

10.5626/JOK.2021.48.2.195

latent space data selection training data Autoencoder Machine Learning

Despite recent significant performance improvements, the iterative process of machine-learning algorithms makes development and utilization difficult and time-consuming. In this paper, we present a data-selection method that reduces the time required by providing an approximate solution . First, data are mapped to a feature vector in latent space based on an Autoencoder, with high weight given to data with high learning contribution that are relatively difficult to learn. Finally, data are ranked and selected based on weight and used for training. Experimental results showed that the proposed method selected data that achieve higher performance than random sampling.