# EE-AE: An Exclusivity Enhanced Unsupervised Feature Learning Approach

## TL;DR

This paper introduces EE-AE, an innovative unsupervised autoencoder method that incorporates an exclusivity concept to enhance feature learning, addressing overfitting and improving robustness and discriminability of data representations.

## Contribution

It is the first to integrate the exclusivity concept into autoencoder-based unsupervised feature learning, along with improvements to stacked AE structures for better layer connection.

## Key findings

- Achieves superior performance over existing methods.
- Effectively reduces overfitting in autoencoder training.
- Enhances robustness and discriminability of learned features.

## Abstract

Unsupervised learning is becoming more and more important recently. As one of its key components, the autoencoder (AE) aims to learn a latent feature representation of data which is more robust and discriminative. However, most AE based methods only focus on the reconstruction within the encoder-decoder phase, which ignores the inherent relation of data, i.e., statistical and geometrical dependence, and easily causes overfitting. In order to deal with this issue, we propose an Exclusivity Enhanced (EE) unsupervised feature learning approach to improve the conventional AE. To the best of our knowledge, our research is the first to utilize such exclusivity concept to cooperate with feature extraction within AE. Moreover, in this paper we also make some improvements to the stacked AE structure especially for the connection of different layers from decoders, this could be regarded as a weight initialization trial. The experimental results show that our proposed approach can achieve remarkable performance compared with other related methods.