Results 71 to 80 of about 8,286,998 (397)
DimenFix: A novel meta-dimensionality reduction method for feature preservation [PDF]
arXiv, 2022 Dimensionality reduction has become an important research topic as demand for interpreting high-dimensional datasets has been increasing rapidly in recent years. There have been many dimensionality reduction methods with good performance in preserving the overall relationship among data points when mapping them to a lower-dimensional space.
arxiv
Dimensional Reduction in Non-Supersymmetric Theories [PDF]
Z. Phys. C62 (1994) 161-166, 1993 It is shown that regularisation by dimensional reduction is a viable alternative to dimensional regularisation in non-supersymmetric theories.
arxiv +1 more source
Network topology drives population temporal variability in experimental habitat networks
Population Ecology, EarlyView.Habitat patches connected by dispersal pathways form habitat networks. We explored how network topology affects population outcomes in laboratory experiments using a model species (Daphnia carinata). Central habitat nodes in complex lattice networks exhibited lower temporal variability in population sizes, suggesting they support more stable ...
Yiwen Xu +3 more
wiley +1 more source
Quantum resonant dimensionality reduction
Physical Review ResearchQuantum computing is a promising candidate for accelerating machine learning tasks. Limited by the control accuracy of current quantum hardware, reducing the consumption of quantum resources is the key to achieving quantum advantage.
Fan Yang +6 more
doaj +1 more source
FEBS Letters, EarlyView.Metabolic dysfunction‐associated steatotic liver disease (MASLD) affects nearly one‐third of the global population and poses a significant risk of progression to cirrhosis or liver cancer. Here, we discuss the roles of hepatic dendritic cell subtypes in MASLD, highlighting their distinct contributions to disease initiation and progression, and their ...
Camilla Klaimi +3 more
wiley +1 more source
Dimensionality Reduction by Similarity Distance-Based Hypergraph Embedding
Atmosphere, 2022 Dimensionality reduction (DR) is an essential pre-processing step for hyperspectral image processing and analysis. However, the complex relationship among several sample clusters, which reveals more intrinsic information about samples but cannot be ...
Xingchen Shen, Shixu Fang, Wenwen Qiang
doaj +1 more source
A gap between two approaches of dimensional reduction for a six-dimensional Kaluza-Klein theory [PDF]
Eur. Phys. J. Plus 140, 149 (2025)Inspired by the five-dimensional Kaluza-Klein theory, we would like to study the dimensional reduction issue of six-dimensional Kaluza-Klein extension in this paper. In particular, we will examine two possible approaches of dimensional reduction from six-dimensional spacetimes to four-dimensional ones. The first one is a direct dimensional reduction, i.
arxiv +1 more source
On the consistency of coset space dimensional reduction [PDF]
Phys.Lett.B656:152-157,2007, 2007 In this letter we consider higher-dimensional Yang-Mills theories and examine their consistent coset space dimensional reduction. Utilizing a suitable ansatz and imposing a simple set of constraints we determine the four-dimensional gauge theory obtained from the reduction of both the higher-dimensional Lagrangian and the corresponding equations of ...
arxiv +1 more source
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning [PDF]
IEEE Transactions on Visualization and Computer Graphics, 2019 Dimensionality reduction (DR) is frequently used for analyzing and visualizing high-dimensional data as it provides a good first glance of the data. However, to interpret the DR result for gaining useful insights from the data, it would take additional ...
Takanori Fujiwara, Oh-Hyun Kwon, K. Ma
semanticscholar +1 more source
Isometric sketching of any set via the Restricted Isometry Property [PDF]
, 2015 In this paper we show that for the purposes of dimensionality reduction certain class of structured random matrices behave similarly to random Gaussian matrices. This class includes several matrices for which matrix-vector multiply can be computed in log-
Oymak, Samet +2 more
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