Review of clustering technology and its application in coordinating vehicle subsystems
Clustering is an unsupervised learning technology, and it groups information (observations
or datasets) according to similarity measures. Developing clustering algorithms is a hot topic …
or datasets) according to similarity measures. Developing clustering algorithms is a hot topic …
Unsupervised affinity learning based on manifold analysis for image retrieval: A survey
VH Pereira-Ferrero, TG Lewis, LP Valem… - Computer Science …, 2024 - Elsevier
Despite the advances in machine learning techniques, similarity assessment among
multimedia data remains a challenging task of broad interest in computer science …
multimedia data remains a challenging task of broad interest in computer science …
A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder
M Yang, M Zhao, D Huang, X Su - Renewable Energy, 2022 - Elsevier
The improvement of photovoltaic (PV) power prediction precision plays a crucial role in the
new energy consumption. This paper proposes a composite prediction framework (DC (DWT …
new energy consumption. This paper proposes a composite prediction framework (DC (DWT …
Feature augmentation based on manifold ranking and LSTM for image classification
VH Pereira-Ferrero, LP Valem… - Expert Systems with …, 2023 - Elsevier
Image classification is a critical topic due to its wide application and several challenges
associated. Despite the huge progress made last decades, there is still a demand for context …
associated. Despite the huge progress made last decades, there is still a demand for context …
Improved graph-regularized deep belief network with sparse features learning for fault diagnosis
J Yang, W Bao, X Li, Y Liu - Neural Computing and Applications, 2022 - Springer
Vibration signals are widely used in fault diagnosis of rotating machinery in real-world
situations. However, it is very challenging to extract effective fault features from noisy signals …
situations. However, it is very challenging to extract effective fault features from noisy signals …
Reliable Information Retrieval Systems Performance Evaluation: A Review
With the progressive and availability of various search tools, interest in the evaluation of
information retrieval based on user perspective has grown tremendously among …
information retrieval based on user perspective has grown tremendously among …
pyUDLF: A Python Framework for Unsupervised Distance Learning Tasks
The representation of multimedia content experienced tremendous advances in the last
decades. Mainly supported by deep learning models, impressive results have been …
decades. Mainly supported by deep learning models, impressive results have been …
Riemannian Manifold-Based Feature Space and Corresponding Image Clustering Algorithms
X Zhao, C Li, J Wu, X Li - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Image feature representation is a key factor influencing the accuracy of clustering.
Traditional point-based feature spaces represent spectral features of an image …
Traditional point-based feature spaces represent spectral features of an image …
Aprendizado não supervisionado baseado em ranqueamento e aprendizado profundo em domínios diversos
VHP Ferrero - 2024 - repositorio.unesp.br
Em geral, os sistemas de recuperação de imagens realizam comparações considerando
apenas pares de imagens e acabam por não levar em consideração as informações …
apenas pares de imagens e acabam por não levar em consideração as informações …
[PDF][PDF] Computer Science Review
Despite the advances in machine learning techniques, similarity assessment among
multimedia data remains a challenging task of broad interest in computer science …
multimedia data remains a challenging task of broad interest in computer science …