2.7k citations · 2.8k across the 15 of their papers we have counts for
9 papers · 1 filter
Cluster-Adaptive Feature Extraction and its Theoretical Foundation with Minkowski Weighted k-Means
Renato Cordeiro de Amorim, Vladimir Makarenkov
The Minkowski weighted -means (-means) algorithm extends classical -means by incorporating feature weights and a Minkowski distance. We first show that the -means o…
ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
Mohammadreza Bakhtyari, Bogdan Mazoure, Renato Cordeiro de Amorim +2
Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue. We introduce ClustRecNet, a novel end-to-end deep learning fram…
Improving clustering quality evaluation in noisy Gaussian mixtures
Renato Cordeiro de Amorim, Vladimir Makarenkov
Clustering is a well-established technique in machine learning and data analysis, widely used across various domains. Cluster validity indices, such as the Average Silhouette Width…
Improving cluster recovery with feature rescaling factors
Renato Cordeiro de Amorim, Vladimir Makarenkov
The data preprocessing stage is crucial in clustering. Features may describe entities using different scales. To rectify this, one usually applies feature normalisation aiming at r…
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain +9
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of r…
XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking
Jeremy Charlier, Vladimir Makarenkov
Bootstrap aggregation, known as bagging, is one of the most popular ensemble methods used in machine learning (ML). An ensemble method is a ML method that combines multiple hypothe…
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
Bogdan Mazoure, Thang Doan, Tianyu Li +4
We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional embedding of the policy on a reproducin…
VecHGrad for Solving Accurately Complex Tensor Decomposition
Jeremy Charlier, Vladimir Makarenkov
Tensor decomposition, a collection of factorization techniques for multidimensional arrays, are among the most general and powerful tools for scientific analysis. However, because…
A-Ward_p\b{eta}: Effective hierarchical clustering using the Minkowski metric and a fast k -means initialisation
Renato Cordeiro de Amorim, Vladimir Makarenkov, Boris Mirkin
In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, cal…