Results 1 to 10 of about 26,900 (140)
Contagion Dynamics for Manifold Learning [PDF]
Frontiers in Big Data, 2022 Contagion maps exploit activation times in threshold contagions to assign vectors in high-dimensional Euclidean space to the nodes of a network. A point cloud that is the image of a contagion map reflects both the structure underlying the network and the
Barbara I. Mahler
doaj +5 more sources
Multi-Manifold Learning Fault Diagnosis Method Based on Adaptive Domain Selection and Maximum Manifold Edge [PDF]
SensorsThe vibration signal of rotating machinery is usually nonlinear and non-stationary, and the feature set has information redundancy. Therefore, a high-dimensional feature reduction method based on multi-manifold learning is proposed for rotating machinery
Ling Zhao, Jiawei Ding, Pan Li, Xin Chi
doaj +2 more sources
Comparison of manifold learning algorithms for identifying geochemical anomalies associated with copper mineralization [PDF]
Scientific ReportsThe Baiyin district, situated within the northern Qilian orogenic belt, hosts the largest concentration of copper mineral resources in Gansu Province, Northwestern China. Geochemical anomaly patterns are crucial indicators for mineral exploration in this
Yuwen Min +5 more
doaj +2 more sources
Hierarchical simplicial manifold learning. [PDF]
PNAS NexusAbstract Learning global structures, i.e. topological properties, inherent in complex data is an essential yet challenging task that spans across various scientific and engineering disciplines. A fundamental approach is to extract local data representations and use them to assemble the global structure.
Zhang W, Shih YH, Li JS.
europepmc +3 more sources
Galaxy Evolution with Manifold Learning [PDF]
EntropyMatter in the early Universe was nearly uniform, and galaxies emerged through the gravitational growth of small primordial density fluctuations. Astrophysics has been trying to unveil the complex physical phenomena that have caused the formation and ...
Tsutomu T. Takeuchi +2 more
doaj +2 more sources
A Study on Dimensionality Reduction and Parameters for Hyperspectral Imagery Based on Manifold Learning [PDF]
SensorsWith the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth’s surface objects.
Wenhui Song +5 more
doaj +2 more sources
Unsupervised manifold learning of collective behavior. [PDF]
PLoS Computational Biology, 2021 Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative ...
Mathew Titus +2 more
doaj +2 more sources
IEEE Access, 2021 In recent years, a variety of supervised manifold learning techniques have been proposed to outperform their unsupervised alternative versions in terms of classification accuracy and data structure capturing. Some dissimilarity measures have been used in
Laureta Hajderanj +2 more
doaj +1 more source
eLife, 2022 Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low ...
Corson Areshenkoff +5 more
doaj +1 more source
Adaptive Manifold Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012 Manifold learning algorithms seek to find a low-dimensional parameterization of high-dimensional data. They heavily rely on the notion of what can be considered as local, how accurately the manifold can be approximated locally, and, last but not least, how the local structures can be patched together to produce the global parameterization.
Zhenyue, Zhang +2 more
openaire +2 more sources