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Nonlinear Manifold Learning via Graph Curvature
With the rapid increase of the data, not only the scale of data is very large, but also the dimensionality of the initial data is very high. It is...
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A discriminative multiple-manifold network for image set classification
Because the distinct advantages of manifold-learning methods for feature extraction, Riemannian manifolds have been used extensively in image...
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MMNet: a medical image-to-image translation network based on manifold-value correction and manifold matching
Image-to-image translation (I2I) has broad application prospects for assisting physicians in diagnosis of medical image missing scenarios....
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Multi-view manifold learning of human brain-state trajectories
The complexity of the human brain gives the illusion that brain activity is intrinsically high-dimensional. Nonlinear dimensionality-reduction...
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Manifold Learning Model Reduction in Engineering
This Open Access book reviews recent theoretical and numerical developments in nonlinear model order reduction in continuum mechanics, being...
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Multi-view multi-manifold learning with local and global structure preservation
Most existing multi-view learning methods adopt a single geometrical model to describe multi-class and heterogeneous data on the original feature...
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Human identification based on Gait Manifold
Gait-based pedestrian identification has important applications in intelligent surveillance. From anatomical viewpoint, the physical uniqueness of...
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2D-DLPP Algorithm Based on SPD Manifold Tangent Space
The manifold tangent space-based algorithm has emerged as a promising approach for processing and recognizing high-dimensional data. In this study,...
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Histopathology Image Classification Using Deep Manifold Contrastive Learning
Contrastive learning has gained popularity due to its robustness with good feature representation performance. However, cosine distance, the commonly...
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Manifold learning by a deep Gaussian process autoencoder
The paper presents a novel manifold learning algorithm, the deep Gaussian process autoencoder (DPGA), based on deep Gaussian processes. Deep Gaussian...
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Concept factorization with adaptive graph learning on Stiefel manifold
In machine learning and data mining, concept factorization (CF) has achieved great success for its powerful capability in data representation. To...
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Few-Shot and Transfer Learning with Manifold Distributed Datasets
A manifold distributed dataset with limited labels makes it difficult to train a high-mean accuracy classifier. Transfer learning is beneficial in...
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Manifold-constrained Gaussian process inference for time-varying parameters in dynamic systems
Identification of parameters in ordinary differential equations (ODEs) is an important and challenging task when modeling dynamic systems in...
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Manifold Learning and Graph Neural Network
In this chapter, we will introduce manifold learning and graph neural networks. We hope to introduce graphical probability models as the starting...
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Extending generalized unsupervised manifold alignment
Building connections between different data sets is a fundamental task in machine learning and related application community. With proper manifold...
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A multi-manifold learning based instance weighting and under-sampling for imbalanced data classification problems
Under-sampling is a technique to overcome imbalanced class problem, however, selecting the instances to be dropped and measuring their...
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Robust subspace clustering via two-way manifold representation
Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most...
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Dimensionality reduction by t-Distribution adaptive manifold embedding
High-dimensional data are difficult to explore and analyze due to they are highly correlative and redundant. Although previous dimensionality...
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Convex Hull Collaborative Representation Learning on Grassmann Manifold with \(L_1\) Norm Regularization
Collaborative representation learning mechanism has recently attracted great interest in computer vision and pattern recognition. Previous image set...
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Acoustic data-driven framework for structural defect reconstruction: a manifold learning perspective
Data-driven quantitative defect reconstruction using ultrasonic guided waves has recently demonstrated great potential in the area of non-destructive...
