Results 61 to 70 of about 6,366,089 (278)

Manifold learning based on kernel density estimation

Учёные записки Казанского университета: Серия Физико-математические науки, 2018
The problem of unknown high-dimensional density estimation has been considered. It has been suggested that the support of its measure is a low-dimensional data manifold. This problem arises in many data mining tasks.
A.P. Kuleshov, A.V. Bernstein, Yu.A. Yanovich   +2 more
doaj  

Cerebrospinal Fluid Over Plasma Links Analytes to Cognitive Decline in Older Adults at Risk for Alzheimer's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To identify inflammatory analytes in cerebrospinal fluid (CSF) and plasma associated with cognitive decline in cognitively normal (CN) older adults at risk for Alzheimer's disease (AD). Methods In a longitudinal study of 118 CN older adults (65–80 years, 54% APOE ε4, 26% preclinical AD), 1331 CSF and 1501 plasma analytes were ...
Jagan A. Pillai, Audrey Zhu, Chunxuan Ma, Ming Wang, Yifan Yang, Lynn M. Bekris, Maria Khrestian, Frank P. DiFilippo, Mikhail Nasrallah, Aaron Burberry, James B. Leverenz, Yuan Hou, Feixiong Cheng, Stephen M. Rao   +13 more
wiley   +1 more source

Review on graph learning for dimensionality reduction of hyperspectral image

Geo-spatial Information Science, 2020
Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction
Liangpei Zhang, Fulin Luo
doaj   +1 more source

An Optimization Technique for Linear Manifold Learning-Based Dimensionality Reduction: Evaluations on Hyperspectral Images

Applied Sciences, 2021
Manifold learning tries to find low-dimensional manifolds on high-dimensional data. It is useful to omit redundant data from input. Linear manifold learning algorithms have applicability for out-of-sample data, in which they are fast and practical ...
Ümit Öztürk, Atınç Yılmaz
doaj   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Recovering manifold representations via unsupervised meta-learning

Frontiers in Computer Science
Manifold representation learning holds great promise for theoretical understanding and characterization of deep neural networks' behaviors through the lens of geometries.
Yunye Gong, Jiachen Yao, Ruyi Lian, Xiao Lin, Chao Chen, Ajay Divakaran, Yi Yao   +6 more
doaj   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Diversity Multi-View Clustering With Subspace and NMF-Based Manifold Learning

IEEE Access, 2023
Since the complementarity information among multiple views has been exploited to improve the clustering effect significantly, multi-view clustering has become a hot topic, and many multi-view clustering methods have emerged.
Jiaman Ding, Xiaojiang Fang, Lianyin Jia, Ying Jiang, Runxin Li   +4 more
doaj   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders

Algorithms, 2019
The aim of manifold learning is to extract low-dimensional manifolds from high-dimensional data. Manifold alignment is a variant of manifold learning that uses two or more datasets that are assumed to represent different high-dimensional representations ...
Fayeem Aziz, Aaron S. W. Wong, Stephan Chalup   +2 more
doaj   +1 more source