# Local Deep-Feature Alignment for Unsupervised Dimension Reduction

## TL;DR

This paper introduces LDFA, an unsupervised deep learning method that combines local auto-encoders and global alignment to improve dimension reduction for visualization, clustering, and classification.

## Contribution

It proposes a novel framework that learns local deep features and aligns them globally, capturing both local and global data characteristics in an unsupervised manner.

## Key findings

- LDFA outperforms several existing dimension reduction techniques.
- LDFA effectively captures local and global data structures.
- The method is versatile for visualization, clustering, and classification tasks.

## Abstract

This paper presents an unsupervised deep-learning framework named Local Deep-Feature Alignment (LDFA) for dimension reduction. We construct neighbourhood for each data sample and learn a local Stacked Contractive Auto-encoder (SCAE) from the neighbourhood to extract the local deep features. Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features. Moreover, we derive an approach from LDFA to map explicitly a new data sample into the learned low-dimensional subspace. The advantage of the LDFA method is that it learns both local and global characteristics of the data sample set: the local SCAEs capture local characteristics contained in the data set, while the global alignment procedures encode the interdependencies between neighbourhoods into the final low-dimensional feature representations. Experimental results on data visualization, clustering and classification show that the LDFA method is competitive with several well-known dimension reduction techniques, and exploiting locality in deep learning is a research topic worth further exploring.