Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from
data mining, recommendation system and Internet of Things, to Industry 4.0 framework …

k-nearest neighbor (k NN) is a widely used learning algorithm for supervised learning tasks.
In practice, the main challenge when using k NN is its high sensitivity to its hyperparameter …

Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks,
which is highly related to geo-energy exploration and evaluation. Conventional physics …

Metric learning can be very useful to improve the performance of a distance-dependent
classifier. However, separating metric learning from the classifier learning possibly …

The fuzzy k-nearest neighbor (FKNN) algorithm, one of the most well-known and effective
supervised learning techniques, has often been used in data classification problems but …

Over the past decades, distance metric learning has attracted a lot of interest in machine
learning and related fields. In this work, we propose an optimization framework for distance …

Finding an appropriate distance metric that accurately reflects the (dis) similarity between
examples is a key to the success of k-means clustering. While it is not always an easy task to …

With the development of deep learning techniques, fusion of deep features has
demonstrated the powerful capability to improve recognition performance. However, most …

The k-nearest neighbor (KNN) algorithm has been widely used in pattern recognition,
regression, outlier detection and other data mining areas. However, it suffers from the large …