Geodesic distances in the intrinsic dimensionality estimation using packing numbers
Nonlinear Analysis, 2014 Dimensionality reduction is a very important tool in data mining. An intrinsic dimensionality of a data set is a key parameter in many dimensionality reduction algorithms. When the intrinsic dimensionality of a data set is known, it is possible to reduce
Rasa Karbauskaitė, Gintautas Dzemyda
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Non‐linear dimensionality reduction using fuzzy lattices
IET Computer Vision, 2013 The proposed method is based on extraction of non‐linearity from the nearest neighbourhood elements of image. To detect non‐linearity, relation between the nearest neighbourhood elements of the image, have been expressed in terms of Gaussian membership ...
Rajiv Kapoor, Rashmi Gupta
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Using Dimensionality Reduction to Analyze Protein Trajectories
Frontiers in Molecular Biosciences, 2019 In recent years the analysis of molecular dynamics trajectories using dimensionality reduction algorithms has become commonplace. These algorithms seek to find a low-dimensional representation of a trajectory that is, according to a well-defined ...
Gareth A. Tribello, Piero Gasparotto
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Morphological mapping for non‐linear dimensionality reduction
IET Computer Vision, 2015 Recently, much research has been carried out on dimensionality reduction techniques that summarise a large set of features into a smaller set, leading to much less redundancy.
Rajiv Kapoor, Rashmi Gupta
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The class of infinite dimensional quasipolaydic equality algebras is not finitely axiomatizable over its diagonal free reducts [PDF]
arXiv, 2013 We show that the class of infinite dimensional quasipolaydic equality algebras is not finitely axiomatizable over its diagonal free ...
arxiv
Spontaneous Dimensional Reduction in Quantum Gravity [PDF]
, 2016 Hints from a number of different approaches to quantum gravity point to a phenomenon of "spontaneous dimensional reduction" to two spacetime dimensions near the Planck scale. I examine the physical meaning of the term "dimension" in this context, summarize the evidence for dimensional reduction, and discuss possible physical explanations.
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A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks [PDF]
arXiv, 2021 Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality. High-dimensional data are usually in a coherent structure and make the data in relatively small true degrees of freedom. There are global and local dimensionality reduction methods to alleviate the problem.
arxiv
Joint Dimensionality Reduction for Separable Embedding Estimation [PDF]
arXiv, 2021 Low-dimensional embeddings for data from disparate sources play critical roles in multi-modal machine learning, multimedia information retrieval, and bioinformatics. In this paper, we propose a supervised dimensionality reduction method that learns linear embeddings jointly for two feature vectors representing data of different modalities or data from ...
arxiv
Two-Stage Dimensionality Reduction for Social Media Engagement Classification
Applied SciencesThe high dimensionality of real-life datasets is one of the biggest challenges in the machine learning field. Due to the increased need for computational resources, the higher the dimension of the input data is, the more difficult the learning task will ...
Jose Luis Vieira Sobrinho +2 more
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Arabic L2 readability assessment: Dimensionality reduction study
Journal of King Saud University: Computer and Information Sciences, 2022 Readability is a measure that associates a written text to a reader’s skill or grade level. Readability assessment is very important in the field of second or foreign language (L2) learning.
Naoual Nassiri +2 more
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