Results 261 to 270 of about 36,894 (295)

A Soft Mechanoluminescent Skin for High‐Resolution Optical Tactile Sensing in Human–Machine Interaction

Advanced Science, EarlyView.
A soft mechanoluminescent tactile sensor that converts force directly into light is presented, enabling imaging‐based, wiring‐free touch sensing. By integrating a flexible ML‐skin with CMOS readout, the system achieves high sensitivity, fast response, and high spatial resolution, while maintaining structural simplicity and energy efficiency, offering a
Yu Feng, Qiaojiao Wang, Yehui Liu, Jiankun Li, Senlin Hou, Xiaomeng Yang, Ziyi Li, Yanji Yi, Meng Chen, Guanglie Zhang, Hao Sun, Wen Jung Li   +11 more
wiley   +1 more source

Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies. [PDF]

Aging (Albany NY)
Xiong Y, Li S, Bai Y, Chen T, Sun W, Chen L, Yu J, Sun L, Li C, Wang J, Wu B.   +10 more
europepmc   +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, Jing Wang, Hongyuan Zha   +2 more
exaly   +4 more sources

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Riemannian Manifold Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional data lie on an intrinsically low-dimensional Riemannian manifold.
Hongbin Zha
exaly   +3 more sources

Curvature-aware manifold learning [PDF]

Pattern Recognition, 2018
Traditional manifold learning algorithms assumed that the embedded manifold is globally or locally isometric to Euclidean space. Under this assumption, they divided manifold into a set of overlapping local patches which are locally isometric to linear subsets of Euclidean space. By analyzing the global or local isometry assumptions it can be shown that
Yangyang Li
exaly   +3 more sources

Enhance explainability of manifold learning

Neurocomputing, 2022
Henry Han, Wentian Li, Jiacun Wang
exaly   +2 more sources

Geometric Manifold Learning

IEEE Signal Processing Magazine, 2011
We present algorithms for analyzing massive and high-dimensional data sets motivated by theorems from geometry and topology. Optimization criteria for computing data projections are discussed and skew radial basis functions (sRBFs) for constructing nonlinear mappings with sharp transitions are demonstrated.
Jamshidi, Arta, Kirby, Michael, Broomhead, Dave   +2 more
openaire   +2 more sources

Active learning on manifolds

Neurocomputing, 2014
Due to the rapid growth of the size of the digital information available, it is often impossible to label all the samples. Thus, it is crucial to select the most informative samples to label so that the learning performance can be most improved with limited labels. Many active learning algorithms have been proposed for this purpose.
Cheng Li, Haifeng Liu 0001, Deng Cai 0001   +2 more
openaire   +1 more source

Learning Invariance Manifolds

Neurocomputing, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources