Asymmetric weighted logistic metric learning for hyperspectral target detection
Traditional target detection methods assume that the background spectrum is subject to the
Gaussian distribution, which may only perform well under certain conditions. In addition …
Gaussian distribution, which may only perform well under certain conditions. In addition …
Pattern separation network based on the hippocampus activity for handwritten recognition
N Modhej, A Bastanfard, M Teshnehlab… - IEEE …, 2020 - ieeexplore.ieee.org
Reaching high accuracy in handwritten character recognition is an essential challenge since
it is widely used in many fields such as signature analysis and forgery detection. Recently …
it is widely used in many fields such as signature analysis and forgery detection. Recently …
SSMD: Dimensionality reduction and classification of hyperspectral images based on spatial–spectral manifold distance metric learning
Metric learning, which aims to obtain a metric matrix M such that samples from the same
class are close to one another and samples of different classes are far from one another, is …
class are close to one another and samples of different classes are far from one another, is …
Deep feature fusion through adaptive discriminative metric learning for scene recognition
C Wang, G Peng, B De Baets - Information Fusion, 2020 - Elsevier
With the development of deep learning techniques, fusion of deep features has
demonstrated the powerful capability to improve recognition performance. However, most …
demonstrated the powerful capability to improve recognition performance. However, most …
Multi-view distance metric learning via independent and shared feature subspace with applications to face and forest fire recognition, and remote sensing …
Y Yu, L Fu, Y Cheng, Q Ye - Knowledge-Based Systems, 2022 - Elsevier
Abstract Distance Metric Learning for Large Margin Nearest Neighbor (LMNN), as a classic
distance metric learning (DML) method, has attracted much attention among researchers …
distance metric learning (DML) method, has attracted much attention among researchers …
Multi-proxy based deep metric learning
PPK Chan, S Li, J Deng, DS Yeung - Information Sciences, 2023 - Elsevier
Deep metric learning (DML) achieves excellent performance in many open-set scenarios.
However, the current multi-proxy methods rely on a classification framework and the …
However, the current multi-proxy methods rely on a classification framework and the …
Clustered multiple manifold metric learning for hyperspectral image dimensionality reduction and classification
Dimensionality reduction (DR) technology is an important part of hyperspectral image (HSI)
processing. The DR technology can effectively remove the redundant information in the HSIs …
processing. The DR technology can effectively remove the redundant information in the HSIs …
Distance metric learning based on the class center and nearest neighbor relationship
Y Zhao, L Yang - Neural Networks, 2023 - Elsevier
Distance metric learning has been a promising technology to improve the performance of
algorithms related to distance metrics. The existing distance metric learning methods are …
algorithms related to distance metrics. The existing distance metric learning methods are …
Multiple metric learning via local metric fusion
Adaptive distance metric learning based on the characteristics of data can significantly
improve the learner's performance. Due to the limitations of single metric learning for …
improve the learner's performance. Due to the limitations of single metric learning for …
Low-rank supervised and semi-supervised multi-metric learning for classification
P Sun, L Yang - Knowledge-Based Systems, 2022 - Elsevier
Multi-metric learning is an important technique for improving classification performance
since learning a single metric is usually insufficient for complex data. Most of the existing …
since learning a single metric is usually insufficient for complex data. Most of the existing …