A survey on metric learning for feature vectors and structured data
The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …
Graph-based semi-supervised learning: A review
Y Chong, Y Ding, Q Yan, S Pan - Neurocomputing, 2020 - Elsevier
Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …
Weakly supervised deep metric learning for community-contributed image retrieval
Recent years have witnessed the explosive growth of community-contributed images with
rich context information, which is beneficial to the task of image retrieval. It can help us to …
rich context information, which is beneficial to the task of image retrieval. It can help us to …
Semi-supervised local multi-manifold isomap by linear embedding for feature extraction
In this paper, we mainly propose a semi-supervised local multi-manifold Isomap learning
framework by linear embedding, termed SSMM-Isomap, that can apply the labeled and …
framework by linear embedding, termed SSMM-Isomap, that can apply the labeled and …
Data-driven shape analysis and processing
Data-driven methods serve an increasingly important role in discovering geometric,
structural, and semantic relationships between shapes. In contrast to traditional approaches …
structural, and semantic relationships between shapes. In contrast to traditional approaches …
Survey and experimental study on metric learning methods
D Li, Y Tian - Neural networks, 2018 - Elsevier
Distance metric learning has been a hot research spot recently due to its high effectiveness
and efficiency in improving the performance of distance related methods, such as k nearest …
and efficiency in improving the performance of distance related methods, such as k nearest …
Decomposition-based transfer distance metric learning for image classification
Distance metric learning (DML) is a critical factor for image analysis and pattern recognition.
To learn a robust distance metric for a target task, we need abundant side information (ie …
To learn a robust distance metric for a target task, we need abundant side information (ie …
[HTML][HTML] Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals
Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a
mono block centrifugal pump and hence, the detection and diagnosis of these mechanical …
mono block centrifugal pump and hence, the detection and diagnosis of these mechanical …
A kernel classification framework for metric learning
Learning a distance metric from the given training samples plays a crucial role in many
machine learning tasks, and various models and optimization algorithms have been …
machine learning tasks, and various models and optimization algorithms have been …