Beyond linear subspace clustering: A comparative study of nonlinear manifold clustering algorithms
M Abdolali, N Gillis - Computer Science Review, 2021 - Elsevier
Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …
assumption that the high-dimensional data points are approximately distributed around …
Joint unsupervised contrastive learning and robust GMM for text clustering
C Hu, T Wu, S Liu, C Liu, T Ma, F Yang - Information Processing & …, 2024 - Elsevier
Text clustering aims to organize a vast collection of documents into meaningful and coherent
clusters, thereby facilitating the extraction of valuable insights. While current frameworks for …
clusters, thereby facilitating the extraction of valuable insights. While current frameworks for …
Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating
S Zhong, X Wang, J Zhao, W Li, H Li, Y Wang, S Deng… - Applied Energy, 2021 - Elsevier
Applications of electric heating, which can improve carbon emission reduction and
renewable energy utilization, have brought new challenges to the safe operation of energy …
renewable energy utilization, have brought new challenges to the safe operation of energy …
A hybrid deep belief network-based label distribution learning system for seismic damage estimation of liquid storage tanks
Liquid storage tanks play a vital role in the modern chemical process industry (CPI). The
strong ground motion caused by large-scale earthquakes may easily impose severe …
strong ground motion caused by large-scale earthquakes may easily impose severe …
EDCWRN: efficient deep clustering with the weight of representations and the help of neighbors
In existing deep clustering methods, it is assumed that all generated representations are
equally important during the clustering procedure. However, if the model can't learn proper …
equally important during the clustering procedure. However, if the model can't learn proper …
Semi-supervised t-SNE with multi-scale neighborhood preservation
Unsupervised dimensionality reduction (DR) aims to preserve input data structure in a low-
dimensional (LD) space based on neighborhood information. In contrast, supervised DR …
dimensional (LD) space based on neighborhood information. In contrast, supervised DR …
ASVMK: A novel SVMs Kernel based on Apollonius function and density peak clustering
Abstract Support Vector Machine (SVM) is one of the successful methods of machine
learning. SVM uses kernel tricks to efficiently learn non-linear classification tasks. The kernel …
learning. SVM uses kernel tricks to efficiently learn non-linear classification tasks. The kernel …
A geometric-based clustering method using natural neighbors
S Pourbahrami, M Hashemzadeh - Information Sciences, 2022 - Elsevier
Neighborhood-based and density-based clustering methods are applied in various data
analysis applications. However, most of them have low performance in mixed …
analysis applications. However, most of them have low performance in mixed …
A selection metric for semi-supervised learning based on neighborhood construction
The present paper focuses on semi-supervised classification problems. Semi-supervised
learning is a learning task through both labeled and unlabeled samples. One of the main …
learning is a learning task through both labeled and unlabeled samples. One of the main …
Parameter-free surrounding neighborhood based regression methods
T İnkaya - Expert Systems with Applications, 2022 - Elsevier
In machine learning, nearest neighbor (NN) regression is one of the most prominent
methods for numeric prediction. It estimates the output variable of a new data point by …
methods for numeric prediction. It estimates the output variable of a new data point by …