# Auto-weighted Mutli-view Sparse Reconstructive Embedding

## TL;DR

This paper introduces AMSRE, a novel multi-view dimensionality reduction method that leverages sparse reconstructive correlations and auto-weighted view contributions to improve low-dimensional representations of high-dimensional multi-view data.

## Contribution

The paper proposes AMSRE, a new algorithm that exploits sparse reconstructive correlations and auto-weights multiple views for enhanced multi-view data embedding.

## Key findings

- AMSRE outperforms existing methods in experiments.
- It effectively captures complementary information from multiple views.
- The auto-weighted mechanism improves discriminative power.

## Abstract

With the development of multimedia era, multi-view data is generated in various fields. Contrast with those single-view data, multi-view data brings more useful information and should be carefully excavated. Therefore, it is essential to fully exploit the complementary information embedded in multiple views to enhance the performances of many tasks. Especially for those high-dimensional data, how to develop a multi-view dimension reduction algorithm to obtain the low-dimensional representations is of vital importance but chanllenging. In this paper, we propose a novel multi-view dimensional reduction algorithm named Auto-weighted Mutli-view Sparse Reconstructive Embedding (AMSRE) to deal with this problem. AMSRE fully exploits the sparse reconstructive correlations between features from multiple views. Furthermore, it is equipped with an auto-weighted technique to treat multiple views discriminatively according to their contributions. Various experiments have verified the excellent performances of the proposed AMSRE.