Multilinear Graph Embedding: Representation and Regularization for Images.

Area of Science:

• Computer Vision
• Machine Learning
• Data Science

Background:

• Representing multifactor images with hidden latent factors is challenging.
• High-order singular value decomposition (HOSVD) inadequately interprets local variations in multifactor images.
• Existing multilinear models often fail to capture intricate data structures.

Purpose of the Study:

• To introduce a novel method, multilinear graph embedding (MGE), and its kernelized version, MKGE.
• To leverage manifold learning within multilinear models for improved image representation.
• To enhance dimensionality reduction techniques by linking linear, nonlinear, and multilinear approaches.

Main Methods:

• Developed multilinear graph embedding (MGE) and kernelized multilinear graph embedding (MKGE).
• Integrated manifold learning principles into multilinear models.
• Utilized high-order tensors to represent images for supervised MGE.

Main Results:

• MGE and MKGE demonstrated superior performance in face and gait recognition tasks.
• The proposed methods showed significant improvements in image and tensor completion.
• MGE effectively encodes image priors for regularization of high-order tensor data.

Conclusions:

• MGE provides a more effective representation for multifactor images than traditional methods like HOSVD.
• The kernelized version, MKGE, extends the applicability of MGE to nonlinear scenarios.
• MGE shows significant potential for image regularization and completion applications.