Results 41 to 50 of about 36,894 (295)

An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals

IEEE Access, 2020
Modeling data generated by physiological systems is a crucial step in many problems such as classification, signal reconstruction and data augmentation.
Lorenzo Manoni, Claudio Turchetti, Laura Falaschetti   +2 more
doaj   +1 more source

Manifold Learning and Nonlinear Homogenization

Multiscale Modeling & Simulation, 2022
We describe an efficient domain decomposition-based framework for nonlinear multiscale PDE problems. The framework is inspired by manifold learning techniques and exploits the tangent spaces spanned by the nearest neighbors to compress local solution manifolds.
Shi Chen 0003, Qin Li 0007, Jianfeng Lu 0001, Stephen J. Wright 0001   +3 more
openaire   +2 more sources

Prototype Regularized Manifold Regularization Technique for Semi-Supervised Online Extreme Learning Machine

Sensors, 2022
Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible.
Muhammad Zafran Muhammad Zaly Shah, Anazida Zainal, Fuad A. Ghaleb, Abdulrahman Al-Qarafi, Faisal Saeed   +4 more
doaj   +1 more source

Perturbing low dimensional activity manifolds in spiking neuronal networks.

PLoS Computational Biology, 2019
Several recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative ...
Emil Wärnberg, Arvind Kumar
doaj   +1 more source

Multilabel Learning with Incomplete Using Dual-Manifold Mapping [PDF]

Jisuanji gongcheng
In multilabel learning, the classification performance can be improved through the effective use of label correlations. However, owing to the subjectivity of manual tagging and the similarity of label semantics in practical applications, an incomplete ...
XU Zhilei, HUANG Rui
doaj   +1 more source

Adaptive Safe Semi-Supervised Extreme Machine Learning

IEEE Access, 2019
Semi-supervised learning (SSL) based on manifold regularization (MR) is an excellent learning framework. However, the performance of SSL heavily depends on the construction of manifold graph and the safety degrees of unlabeled samples.
Jun Ma, Chao Yuan
doaj   +1 more source

Functorial Manifold Learning

, 2022
We adapt previous research on category theory and topological unsupervised learning to develop a functorial perspective on manifold learning, also known as nonlinear dimensionality reduction. We first characterize manifold learning algorithms as functors
Shiebler, Dan
core   +1 more source

Metric Learning on Manifolds

CoRR, 2019
Recent literature has shown that symbolic data, such as text and graphs, is often better represented by points on a curved manifold, rather than in Euclidean space. However, geometrical operations on manifolds are generally more complicated than in Euclidean space, and thus many techniques for processing and analysis taken for granted in Euclidean ...
Max Aalto, Nakul Verma
openaire   +2 more sources

Learning on manifolds without manifold learning

Neural Networks
Function approximation based on data drawn randomly from an unknown distribution is an important problem in machine learning. The manifold hypothesis assumes that the data is sampled from an unknown submanifold of a high dimensional Euclidean space.
Hrushikesh N. Mhaskar, Ryan O'Dowd
openaire   +3 more sources

Graph Regularized Variational Ladder Networks for Semi-Supervised Learning

IEEE Access, 2020
To tackle the problem of semi-supervised learning (SSL), we propose a new autoencoder-based deep model. Ladder networks (LN) is an autoencoder-based method for representation learning which has been successfully applied on unsupervised learning and semi ...
Cong Hu, Xiao-Ning Song
doaj   +1 more source