# Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)

## TL;DR

This paper introduces a robust spectral clustering method that combines deep autoencoder learning within an ensemble framework, improving clustering stability and performance over existing deep clustering techniques.

## Contribution

The paper proposes a novel ensemble deep autoencoder approach integrated with spectral clustering to enhance robustness and accuracy in data clustering tasks.

## Key findings

- Outperforms state-of-the-art deep clustering methods on benchmark datasets
- Demonstrates increased robustness to hyperparameter settings
- Shows improved clustering stability and accuracy

## Abstract

Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential way, where a deep representation is learned using a deep autoencoder before obtaining clusters with k-means, or a simultaneous way, where deep representation and clusters are learned jointly by optimizing a single objective function. Both strategies improve clustering performance, however the robustness of these approaches is impeded by several deep autoencoder setting issues, among which the weights initialization, the width and number of layers or the number of epochs. To alleviate the impact of such hyperparameters setting on the clustering performance, we propose a new model which combines the spectral clustering and deep autoencoder strengths in an ensemble learning framework. Extensive experiments on various benchmark datasets demonstrate the potential and robustness of our approach compared to state-of-the-art deep clustering methods.