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Vertex transitivity and distance metric of the quad-cube
The quad-cube is a special case of the metacube that itself is derivable from the hypercube. It is amenable to an application as a network topology,...
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A semi-supervised medical image classification method based on combined pseudo-labeling and distance metric consistency
In medical image analysis, obtaining high-quality labeled data is expensive, and there is a large amount of unlabeled image data that is not...
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Metric Violation Distance: Hardness and Approximation
Metric data plays an important role in various settings, for example, in metric-based indexing, clustering, classification, and approximation...
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Fast instance selection method for SVM training based on fuzzy distance metric
Support Vector Machine (SVM) is a well-known classification technique which has achieved excellent performance in many nonlinear and high dimensional...
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Classification of endoscopic image and video frames using distance metric-based learning with interpolated latent features
Conventional Endoscopy (CE) and Wireless Capsule Endoscopy (WCE) are well known tools for diagnosing gastrointestinal (GI) tract related disorders....
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Absolute Variation Distance: An Inversion Attack Evaluation Metric for Federated Learning
Federated Learning (FL) has emerged as a pivotal approach for training models on decentralized data sources by sharing only model gradients. However,...
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Cancelable biometric schemes for Euclidean metric and Cosine metric
The handy biometric data is a double-edged sword, paving the way of the prosperity of biometric authentication systems but bringing the personal...
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Deep Metric Learning
It was mentioned in Chap. 11 that metric learning can be divided into spectral, probabilistic, and deep...
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Spectral Metric Learning
A family of dimensionality reduction methods known as metric learning learns a distance metric in an embedding space to separate dissimilar points...
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FeatEMD: Better Patch Sampling and Distance Metric for Few-Shot Image Classification
Few-shot image classification (FSIC) studies the problem of classifying images when given only a few training samples, which presents a challenge for...
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Boundary-restricted metric learning
Metric learning aims to learn a distance metric to properly measure the similarities between pairwise examples. Most existing learning algorithms are...
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Antipodal two-weight rank metric codes
We consider the class of linear antipodal two-weight rank metric codes and discuss their properties and characterization in terms of t -spreads. It is...
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Understanding More Types of Social Relationships Using Clothing and Distance Metric Learning
Image-based social relationships classification is an emerging and challenging problem in social media analysis. Practically, social relationships...
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Vertex transitivity, distance metric, and hierarchical structure of the dual-cube
The dual-cube, derivable from the hypercube, admits a number of good properties that render it as a good network topology, especially when the node...
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Distance metric learning for graph structured data
Graphs are versatile tools for representing structured data. As a result, a variety of machine learning methods have been studied for graph data...
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Isochronous Temporal Metric for Neighbourhood Analysis in Classification Tasks
Machine learning, classification, and clustering techniques use the distance functions to evaluate the proximity between data entries and deduce the...
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A multi-metric small sphere large margin method for classification
Multi-metric learning is important for improving performance of learners. For complex data, multi metric learning algorithms need intensive research....
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An efficient similarity metric for 3D medical image registration
In this paper, we develop an efficient mutual information based similarity metric for 3D medical image registration. The efficiency of the metric...
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Ordinal regression with explainable distance metric learning based on ordered sequences
The purpose of this paper is to introduce a new distance metric learning algorithm for ordinal regression. Ordinal regression addresses the problem...
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Metric learning and local enhancement based collaborative representation for hyperspectral image classification
Collaborative Representation (CR) models have been successfully employed for Hyperspectral Images (HSIs) classification because of the effectiveness...
