Results 21 to 30 of about 26,484 (294)

Research on a multivariate measurement system for polyethylene gas pipelines utilizing a variable weight UMAP model [PDF]

Scientific Reports
In producing gas-polyethylene pipelines, the five major quality indicators—wall thickness, inner diameter, outer diameter, concentricity, and ovality—exhibit complex interactions, making it challenging for traditional methods to comprehensively evaluate ...
Chenjia Zong, Juan Zhou, Qiang Wang, Haiting Zhou, Yun Song, Zhilong Yu   +5 more
doaj   +2 more sources

Nonlinear dimensionality reduction for clustering [PDF]

Pattern Recognition, 2020
Abstract We introduce an approach to divisive hierarchical clustering that is capable of identifying clusters in nonlinear manifolds. This approach uses the isometric mapping (Isomap) to recursively embed (subsets of) the data in one dimension, and then performs a binary partition designed to avoid the splitting of clusters.
Sotiris K. Tasoulis, Nicos G. Pavlidis, Teemu Roos   +2 more
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Nonlinear dimensionality reduction on graphs [PDF]

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017
In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of properly compressing the data while
Yanning Shen, Panagiotis A. Traganitis, Georgios B. Giannakis   +2 more
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An Orthogonal Locality and Globality Dimensionality Reduction Method Based on Twin Eigen Decomposition

IEEE Access, 2021
Dimensionality reduction is a hot research topic in pattern recognition. Traditional dimensionality reduction methods can be separated into linear dimensionality reduction methods and nonlinear dimensionality reduction methods.
Shuzhi Su, Gang Zhu, Yanmin Zhu
doaj   +1 more source

Temporal nonlinear dimensionality reduction [PDF]

The 2011 International Joint Conference on Neural Networks, 2011
Existing Nonlinear dimensionality reduction (NLDR) algorithms make the assumption that distances between observations are uniformly scaled. Unfortunately, with many interesting systems, this assumption does not hold. We present a new technique called Temporal NLDR (TNLDR), which is specifically designed for analyzing the high-dimensional observations ...
Michael Gashler, Tony R. Martinez
openaire   +1 more source

IMMERSIVE VISUALIZATION OF THE QUALITY OF DIMENSIONALITY REDUCTION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013
Dimensionality reduction is the most widely used approach for extracting the most informative low-dimensional features from highdimensional ones. During the last two decades, different techniques (linear and nonlinear) have been proposed by researchers ...
M. Babaee, M. Datcu, G. Rigoll
doaj   +1 more source

Analyzing Grid-Based Direct Quantum Molecular Dynamics Using Non-Linear Dimensionality Reduction

Molecules, 2021
Grid-based schemes for simulating quantum dynamics, such as the multi-configuration time-dependent Hartree (MCTDH) method, provide highly accurate predictions of the coupled nuclear and electronic dynamics in molecular systems.
Gareth W. Richings, Scott Habershon
doaj   +1 more source

NNNPE: non-neighbourhood and neighbourhood preserving embedding

Connection Science, 2022
Manifold learning is an important class of methods for nonlinear dimensionality reduction. Among them, the LLE optimisation goal is to maintain the relationship between local neighbourhoods in the original embedding manifold to reduce dimensionality, and
Kaizhi Chen, Chengpei Le, Shangping Zhong, Longkun Guo, Ge Xu   +4 more
doaj   +1 more source

Dimensionality Reduction Mappings [PDF]

, 2011
A wealth of powerful dimensionality reduction methods has been established which can be used for data visualization and preprocessing. These are accompanied by formal evaluation schemes, which allow a quantitative evaluation along general principles and ...
Hammer, Barbara ; https://orcid.org/, Hammer, Barbara,, Kerstin Bunte, Michael Biehl, Bunte, Kerstin; id_orcid, Bunte, Kerstin,, Barbara Hammer, Biehl, Michael, Bunte, Kerstin, Biehl, Michael,, Biehl, Michael; id_orcid, IEEE Computational Intelligence Society, Hammer, Barbara   +12 more
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Quantum diffusion map for nonlinear dimensionality reduction [PDF]

Physical Review A, 2021
Inspired by random walk on graphs, diffusion map (DM) is a class of unsupervised machine learning that offers automatic identification of low-dimensional data structure hidden in a high-dimensional dataset. In recent years, among its many applications, DM has been successfully applied to discover relevant order parameters in many-body systems, enabling
Apimuk Sornsaeng, Ninnat Dangniam, Pantita Palittapongarnpim, Thiparat Chotibut   +3 more
openaire   +2 more sources