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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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Adaptive affinity matrix learning for dimensionality reduction
Conventional graph-based dimensionality reduction methods treat graph leaning and subspace learning as two separate steps, and fix the graph during...
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Towards a Machine Learning Pipeline in Reduced Order Modelling for Inverse Problems: Neural Networks for Boundary Parametrization, Dimensionality Reduction and Solution Manifold Approximation
In this work, we propose a model order reduction framework to deal with inverse problems in a non-intrusive setting. Inverse problems, especially in...
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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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Manifold-based denoising, outlier detection, and dimension reduction algorithm for high-dimensional data
Manifold learning, which has emerged in recent years, plays an increasingly important role in machine learning. However, because inevitable noises...
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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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Optimizing identification of mine water inrush source with manifold reduction and semi-supervised learning using improved autoencoder
To enhance the accuracy of identifying water sources in mine inrush incidents, this study, taking the Shengquan coal mine in Shandong, China, as a...
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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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Some aspects of nonlinear dimensionality reduction
In this paper we discuss nonlinear dimensionality reduction within the framework of principal curves. We formulate dimensionality reduction as...
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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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Unsupervised manifold embedding to encode molecular quantum information for supervised learning of chemical data
Molecular representation is critical in chemical machine learning. It governs the complexity of model development and the fulfillment of training...
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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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Dimensionality reduction of tensors based on manifold-regularized tucker decomposition and its iterative solution
In recent years, tensor data appear more frequently in machine learning. Tucker decomposition is a powerful tool for processing tensor data. However,...
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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...
