Results 31 to 40 of about 155,627 (235)
Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification
, 2018 Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than ...
Kang, Zhao +3 more
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Multiple Kernel Clustering With Global and Local Structure Alignment
IEEE Access, 2018 Multiple kernel clustering (MKC) based on global structure alignment (GSA) has unified many existing MKC algorithms, and shown outstanding clustering performance.
Chuanli Wang +5 more
doaj +1 more source
Unified Spectral Clustering with Optimal Graph
, 2017 Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering ...
Cheng, Qiang +3 more
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Deep Divergence-Based Approach to Clustering [PDF]
, 2019 A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function.
Bianchi, Filippo M. +5 more
core +3 more sources
Multiple Kernel Subspace Clustering Based on Consensus Hilbert Space and Second-Order Neighbors
IEEE Access, 2020 How to deal with data sets in high-dimensional space is the focus of image processing. At present, subspace clustering method is one of the most commonly used methods for processing high-dimensional data sets. Traditional subspace clustering assumes that
Zhongyuan Wang, Jinglei Liu
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Kernel Spectral Curvature Clustering (KSCC)
, 2009 Multi-manifold modeling is increasingly used in segmentation and data representation tasks in computer vision and related fields. While the general problem, modeling data by mixtures of manifolds, is very challenging, several approaches exist for ...
Atev, S., Chen, G., Lerman, G.
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Entropy, 2020 Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data.
Alessio Martino +3 more
doaj +1 more source
Spectral Clustering with Jensen-type kernels and their multi-point extensions
, 2014 Motivated by multi-distribution divergences, which originate in information theory, we propose a notion of `multi-point' kernels, and study their applications.
Adsul, Ajay P. +3 more
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Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Low‐cycle fatigue damage in Mn–Mo–Ni reactor pressure vessel steel is examined using a combined electron backscatter diffraction and positron annihilation lifetime spectroscopy approach. The study correlates texture evolution, dislocation substructure development, and vacancy‐type defect formation across uniform, necked, and fracture regions, providing
Apu Sarkar +2 more
wiley +1 more source