# Multi-local Collaborative AutoEncoder

## TL;DR

The paper introduces MC-AE, a novel autoencoder that leverages multi-local collaborative relationships in unlabeled data using LSH to improve unsupervised clustering performance.

## Contribution

It proposes a new multi-local collaborative autoencoder incorporating mcrRBM and mcrGRBM models with LSH-based data partitioning for enhanced feature learning.

## Key findings

- Outperforms five related deep models in representation and generalization.
- Effectively captures multi-local collaborative relationships in unlabeled data.
- Enhances unsupervised clustering capabilities.

## Abstract

The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborative relationships of unlabeled data are ignored in their encoding procedure that limits the capability of feature extraction. This paper presents a Multi-local Collaborative AutoEncoder (MC-AE), which consists of novel multi-local collaborative representation RBM (mcrRBM) and multi-local collaborative representation GRBM (mcrGRBM) models. Here, the Locality Sensitive Hashing (LSH) method is used to divide the input data into multi-local cross blocks which contains multi-local collaborative relationships of the unlabeled data and features since the similar multi-local instances and features of the input data are divided into the same block. In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure. Then, the local hidden features converges on the center of each local collaborative block. Under the collaborative joint influence of each local block, the proposed MC-AE has powerful capability of representation learning for unsupervised clustering. However, our MC-AE model perhaps perform training process for a long time on the large-scale and high-dimensional datasets because more local collaborative blocks are integrate into it. Five most related deep models are compared with our MC-AE. The experimental results show that the proposed MC-AE has more excellent capabilities of collaborative representation and generalization than the contrastive deep models.