# Generative approach to unsupervised deep local learning

## TL;DR

This paper introduces a novel generative deep learning framework that pretrains convolutional autoencoders, constructs affinity graphs, and employs a self-expressive layer with a locality-preserving loss to enhance unsupervised feature learning.

## Contribution

It proposes a new generative approach combining autoencoders, affinity graph construction, and a self-expressive layer with a locality-preserving loss for improved unsupervised feature learning.

## Key findings

- Outperforms state-of-the-art methods on four datasets
- Effectively preserves local data structure during learning
- Enhances the quality of learned feature representations

## Abstract

Most existing feature learning methods optimize inflexible handcrafted features and the affinity matrix is constructed by shallow linear embedding methods. Different from these conventional methods, we pretrain a generative neural network by stacking convolutional autoencoders to learn the latent data representation and then construct an affinity graph with them as a prior. Based on the pretrained model and the constructed graph, we add a self-expressive layer to complete the generative model and then fine-tune it with a new loss function, including the reconstruction loss and a deliberately defined locality-preserving loss. The locality-preserving loss designed by the constructed affinity graph serves as prior to preserve the local structure during the fine-tuning stage, which in turn improves the quality of feature representation effectively. Furthermore, the self-expressive layer between the encoder and decoder is based on the assumption that each latent feature is a linear combination of other latent features, so the weighted combination coefficients of the self-expressive layer are used to construct a new refined affinity graph for representing the data structure. We conduct experiments on four datasets to demonstrate the superiority of the representation ability of our proposed model over the state-of-the-art methods.