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{{Orphan|date=April 2025}}
'''Multimodal representation learning''' is a subfield of [[Feature learning|representation learning]] focused on integrating and interpreting information from different [[Modality (human–computer interaction)|modalities]], such as text, images, audio, or video, by projecting them into a shared latent space. This allows for semantically similar content across modalities to be mapped to nearby points within that space, facilitating a unified understanding of diverse data types.<ref name=":0">{{Cite journal |
== Motivation ==
The primary motivations for multimodal representation learning arise from the inherent nature of real-world data and the limitations of unimodal approaches. Since multimodal data offers complementary and supplementary information about an object or event from different perspectives, it is more informative than relying on a single modality.<ref name=":0" /> A key motivation is to narrow the heterogeneity gap that exists between different modalities by projecting their features into a shared semantic subspace. This allows semantically similar content across modalities to be represented by similar vectors, facilitating the understanding of relationships and correlations between them. Multimodal representation learning aims to leverage the unique information provided by each modality to achieve a more comprehensive and accurate understanding of concepts.
These unified representations are crucial for improving performance in various cross-media analysis tasks such as video classification, event detection, and sentiment analysis. They also enable cross-modal retrieval, allowing users to search and retrieve content across different modalities.<ref>{{Cite
== Approaches and methods ==
=== Canonical-correlation analysis based methods ===
[[Canonical correlation|Canonical-correlation analysis]] (CCA) was first introduced in 1936 by [[Harold Hotelling]]<ref>{{Cite journal |last=Hotelling |first=H. |date=1936-12-01 |title=
</math> and <math>w_y\in\mathbb{R}^q </math> that maximizes the correlation between the projected variables:
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</math> memory requirement for sorting kernel matrices.
KCCA was proposed independently by several researchers.<ref>{{Cite journal |last=Lai |first=P |date=October 2000 |title=Kernel and Nonlinear Canonical Correlation Analysis |url=http://linkinghub.elsevier.com/retrieve/pii/S012906570000034X |journal=International Journal of Neural Systems |volume=10 |issue=5 |pages=365–377 |doi=10.1016/S0129-0657(00)00034-X|pmid=11195936 |url-access=subscription }}</ref><ref>{{Cite web |title=Kernel Independent Component Analysis {{!}} EECS at UC Berkeley |url=https://www2.eecs.berkeley.edu/Pubs/TechRpts/2001/5721.html |access-date=2025-04-16 |website=www2.eecs.berkeley.edu}}</ref><ref>{{Cite book |
==== Deep CCA ====
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where <math>H_x=f_x(X)</math> and <math>H_y=f_y(Y)</math> are the network outputs, <math>T=H_x^TH_x+r_xI</math>, <math>S=H_y^TH_y+r_yI
</math> and <math>r_x, r_y</math> are the regularization parameters. DCCA overcomes the limitations of linear CCA and kernel CCA by learning complex nonlinear relationships while maintaining computational efficiency for large datasets through mini-batch optimization.<ref>{{Cite journal |
=== Graph-based methods ===
Graph-based approaches for multimodal representation learning leverage graph structure to model relationships between entities across different modalities. These methods typically represent each modality as a graph and then learn embedding that preserve cross-modal similarities, enabling more effective joint representation of heterogeneous data.<ref>{{Cite journal |
One such method is '''cross-modal graph neural networks''' (CMGNNs) that extend traditional [[graph neural network]]s (GNNs) to handle data from multiple modalities by constructing graphs that capture both intra-modal and inter-modal relationships. These networks model interactions across modalities by representing them as [[Vertex (graph theory)|nodes]] and their relationships as edges.<ref>{{Cite
Other graph-based methods include [[Graphical model|'''Probabilistic Graphical Models''']] (PGMs) such as [[deep belief network]]s (DBN) and deep [[Boltzmann machine]]s (DBM). These models can learn a joint representation across modalities, for instance, a multimodal DBN achieves this by adding a shared restricted Boltzmann Machine (RBM) hidden layer on top of modality-specific DBNs.<ref name=":0" /> Additionally, the structure of data in some domains like [[Human–computer interaction|Human-Computer Interaction]] (HCI), such as the view hierarchy of app screens, can potentially be modeled using graph-like structures. The field of graph representation learning is also relevant, with ongoing progress in developing evaluation benchmarks.<ref>{{Cite journal |
=== Diffusion maps ===
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==== Multi-view diffusion maps ====
Multi-view diffusion maps address the challenge of achieving multi-view dimensionality reduction by effectively utilizing the availability of multiple views to extract a coherent low-dimensional representation of the data. The core idea is to exploit both the intrinsic relations within each view and the mutual relations between the different views, defining a cross-view model where a [[random walk]] process implicitly hops between objects in different views. A multi-view kernel matrix is constructed by combining these relations, defining a cross-view diffusion process and associated diffusion distances. The [[Spectral decomposition (Matrix)|spectral decomposition]] of this kernel enables the discovery of an embedding that better leverages the information from all views. This method has demonstrated utility in various machine learning tasks, including classification, clustering, and manifold learning.<ref>{{Cite journal |
==== Alternating diffusion ====
Alternating diffusion based methods provide another strategy for multimodal representation learning by focusing on extracting the common underlying sources of variability present across multiple views or sensors. These methods aim to filter out sensor-specific or nuisance components, assuming that the phenomenon of interest is captured by two or more sensors. The core idea involves constructing an alternating diffusion operator by sequentially applying diffusion processes derived from each modality, typically through their product or intersection. This process allows the method to capture the structure related to common hidden variables that drive the observed multimodal data.<ref>{{Cite journal |
== See also ==
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== References ==
<references />
[[Category:Machine learning]]
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