# Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval

arXiv: https://arxiv.org/abs/1605.09696 · HTML: https://mlsanity.com/paper/1605.09696/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2016.

## Built on by (most cited first, 6 of 6)

- [Geometric Multimodal Deep Learning with Multi-Scaled Graph Wavelet Convolutional Network](https://mlsanity.com/paper/2111.13361/) (2021)
- [Multi-View Class Incremental Learning](https://mlsanity.com/paper/2306.09675/) (2023)
- [Trace Ratio Optimization with an Application to Multi-view Learning](https://mlsanity.com/paper/2101.04292/) (2021)
- [A unified framework based on graph consensus term for multi-view learning](https://mlsanity.com/paper/2105.11781/) (2021)
- [Multi-view Feature Extraction based on Dual Contrastive Head](https://mlsanity.com/paper/2302.03932/) (2023)
- [Multi-view Feature Extraction based on Triple Contrastive Heads](https://mlsanity.com/paper/2303.12615/) (2023)

## Abstract (arXiv)

In this paper, the problem of multi-view embedding from different visual cues and modalities is considered. We propose a unified solution for subspace learning methods using the Rayleigh quotient, which is extensible for multiple views, supervised learning, and non-linear embeddings. Numerous methods including Canonical Correlation Analysis, Partial Least Sqaure regression and Linear Discriminant Analysis are studied using specific intrinsic and penalty graphs within the same framework. Non-linear extensions based on kernels and (deep) neural networks are derived, achieving better performance than the linear ones. Moreover, a novel Multi-view Modular Discriminant Analysis (MvMDA) is proposed by taking the view difference into consideration. We demonstrate the effectiveness of the proposed multi-view embedding methods on visual object recognition and cross-modal image retrieval, and obtain superior results in both applications compared to related methods.