An Analysis of Image Classification Using Wavelet-Based Autoencoder Architecture and Extreme Learning Machine

Abstract

In recent machine learning applications, promising outcomes have emerged through the integration of Deep Learning (DL) and Extreme Learning Machine (ELM) techniques with wavelet networks (WN), leading to high classification accuracy. Researchers have explored various strategies to enhance classifier performance. Among these methods, Deep Wavelet Autoencoders (AE) and the Deep Wavelet ELM algorithm have been extensively applied in diverse image classification domains. This research paper conducts a comparative analysis between these two wavelet AE-based techniques. The first approach involves the development of a Deep Stacked Sparse Wavelet Autoencoder (DSSWAE), while the second method focuses on creating a Deep Wavelet ELM Autoencoder (DW-ELM-AE). To enable a comprehensive comparison, experiments were conducted using different datasets, specifically MNIST and COIL-20. The results clearly demonstrate that the Deep Stacked Wavelet AE (DSWAE) and DSSWAE algorithms outperform the State-of-the-Art (SOTA) techniques in terms of accuracy efficiency, emphasizing their significant potential for a wide range of image classification applications.