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Trends and challenges in intelligent condition monitoring of electrical machines using machine learning
A review of the fault diagnostic techniques based on machine is presented in this paper. As
the world is moving towards industry 4.0 standards, the problems of limited computational …
the world is moving towards industry 4.0 standards, the problems of limited computational …
Contrastive multi-view kernel learning
Kernel method is a proven technique in multi-view learning. It implicitly defines a Hilbert
space where samples can be linearly separated. Most kernel-based multi-view learning …
space where samples can be linearly separated. Most kernel-based multi-view learning …
Fast incomplete multi-view clustering with view-independent anchors
Multi-view clustering (MVC) methods aim to exploit consistent and complementary
information among each view and achieve encouraging performance improvement than …
information among each view and achieve encouraging performance improvement than …
[PDF][PDF] Multi-view clustering via late fusion alignment maximization.
Multi-view clustering (MVC) optimally integrates complementary information from different
views to improve clustering performance. Although demonstrating promising performance in …
views to improve clustering performance. Although demonstrating promising performance in …
One pass late fusion multi-view clustering
Existing late fusion multi-view clustering (LFMVC) optimally integrates a group of pre-
specified base partition matrices to learn a consensus one. It is then taken as the input of the …
specified base partition matrices to learn a consensus one. It is then taken as the input of the …
A survey on high-dimensional subspace clustering
With the rapid development of science and technology, high-dimensional data have been
widely used in various fields. Due to the complex characteristics of high-dimensional data, it …
widely used in various fields. Due to the complex characteristics of high-dimensional data, it …
Late fusion multiple kernel clustering with proxy graph refinement
Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to
improve clustering performance. Among existing MKC algorithms, the recently proposed late …
improve clustering performance. Among existing MKC algorithms, the recently proposed late …
Structured graph learning for clustering and semi-supervised classification
Graphs have become increasingly popular in modeling structures and interactions in a wide
variety of problems during the last decade. Graph-based clustering and semi-supervised …
variety of problems during the last decade. Graph-based clustering and semi-supervised …
Knowledge-induced multiple kernel fuzzy clustering
The introduction of domain knowledge opens new horizons to fuzzy clustering. Then
knowledge-driven and data-driven fuzzy clustering methods come into being. To address …
knowledge-driven and data-driven fuzzy clustering methods come into being. To address …
Based kernel fuzzy clustering with weight information granules
Domain knowledge can be introduced into fuzzy clustering with the aid of information
granules, embodied by the concept of viewpoints. For such kind of fuzzy clustering methods …
granules, embodied by the concept of viewpoints. For such kind of fuzzy clustering methods …