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Elements of Dimensionality Reduction and Manifold Learning
Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for...
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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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Dimensionality Reduction Based on kCCC and Manifold Learning
This paper first proposes a statistic for measuring the correlation between two random variables. Because the data are usually polluted by noise and...
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Advancing ADHD diagnosis: using machine learning for unveiling ADHD patterns through dimensionality reduction on IoMT actigraphy signals
Mental health is an integral component of overall well-being, profoundly influencing the lives of individuals, families, and communities worldwide....
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Dimensionality reduction of SPD data based on Riemannian manifold tangent spaces and local affinity
Non-Euclidean data is increasingly used in practical applications. As a typical representative, Symmetric Positive Definite (SPD) matrices can form a...
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Dimensionality Reduction
Having more features for inference may be thought of better than having just a handful of features. However, in machine learning, data scientists...
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Multi-view dimensionality reduction learning with hierarchical sparse feature selection
Multi-view data can depict samples from various views and learners can benefit from such complementary information, so it has attracted extensive...
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Dimensionality Reduction
This video introduces you to an exhaustive list of dimensionality reduction techniques that will enable you to reduce the dimensions of your dataset...
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Stabilizing and Simplifying Sharpened Dimensionality Reduction Using Deep Learning
Dimensionality reduction (DR) methods create 2D scatterplots of high-dimensional data for visual exploration. As such scatterplots are often used to...
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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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Linear and Nonlinear Dimensionality Reduction
In this chapter, we start with a simple 2D rotation matrix example to demonstrate the rationale for representing complex phenomena in terms of their...
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Homogeneous ensemble extreme learning machine autoencoder with mutual representation learning and manifold regularization for medical datasets
As a single learner, extreme learning machine autoencoder (ELM-AE) and generalized extreme learning machine autoencoder (GELM-AE) have limited...
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A combination of supervised dimensionality reduction and learning methods to forecast solar radiation
Machine learning is routinely used to forecast solar radiation from inputs, which are forecasts of meteorological variables provided by numerical...
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Hybrid Integrated Dimensionality Reduction Method Based on Conformal Homeomorphism Map**
Based on the theories of Riemannian surface, Topology and Analytic function, a novel method for dimensionality reduction is proposed in this paper....
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Face manifold: manifold learning for synthetic face generation
The face is a crucial aspect of human communication and identity. Accurately estimating face structure is a fundamental task in computer vision, with...
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Interpretable linear dimensionality reduction based on bias-variance analysis
One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should...
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Soft dimensionality reduction for reinforcement data clustering
The standard Euclidean distance considers equal contributions for all features of each data sample pair when computing the similarity matrix, while...
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Laplacian-Based Dimensionality Reduction
Spectral dimensionality reduction methods deal with the graph and geometry of data and usually reduce to an eigenvalue or generalized eigenvalue...
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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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Manifold learning through locally linear reconstruction based on Euclidean distance
In this manuscript, we introduce a novel local manifold learning method that extracts further structural information from the high-dimensional data...
