[36]. Based on labeled training data,Hertz et al.maximized the margin with boosting to obtain distance functions for clustering[37].Bilenko et al. integrated the pairwise constraints (must-links and cannot-links) and metric learning into a semi-supervised clustering[38].Clustering on many data...
Besides, to effectively make use of both the labeled and unlabeled data to enhance the performance of semi-supervised clustering, we propose a KPML-based approach that leverages metric learning and semi-supervised learning effectively in a novel way. Finally, we use our model to do experiments ...
Learning a Mahalanobis distance metric for data clustering and classification Distance metric is a key issue in many machine learning algorithms. This paper considers a general problem of learning from pairwise constraints in the for... S Xiang,F Nie,C Zhang - 《Pattern Recognition》 被引量: 62...
Structured metric learning for high dimensional problems The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the un- derlying distance functions... JV Davis,IS Dhillon - 《Knowledge Discovery & Data Mining》 被引量: 92发表: 2008...
Scalable Large-Margin Mahalanobis Distance Metric Learning For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN) classifiers and $ k $-means clustering, often their success heavily depends on t... C Shen,J Kim,L Wang - 《IEEE Transactions on Neural Networks》 被引量:...
Scalable Large-Margin Mahalanobis Distance Metric Learning For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN) classifiers and $ k $-means clustering, often their success heavily depends on t... C Shen,J Kim,L Wang - 《IEEE Transactions on Neural Networks》 被引量:...
least squares support vector machine (SLSSVM), a novel semi-supervised sparse least squares support vector machine based on Mahalanobis distance (MS-SLSSVM) was proposed, which innovatively introduced the Mahalanobis distance metric learning and the pruning method of geometric clustering into SLSSVM....
This measure is widely used in various fields such as data clustering [37,38] and multivariate diagnosis and pattern recognition [39,40]. Specifically, for a multivariate vector 𝑥=(𝑥1,𝑥2,…,𝑥𝑛)𝑇x=(x1,x2,…,xn)T, mean vector 𝜇=(𝜇1,𝜇2,…,𝜇𝑛)𝑇μ=(μ...
[43] have used nonlinear dimensionality reduction, k-means clustering, and hidden Markov models to recognize AE patterns for containment structures. These structures may suffer from hidden declamation cracks, and it is very hard to detect them. Validation was done using large-scale concrete walls, ...