Kernel-Distance Target Alignment

2014 ◽  
pp. 101-110
Author(s):  
Peiyan Wang ◽  
Cai Dongfeng
Keyword(s):  
Kernel Distance

scholarly journals Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image

2021 ◽  
Vol 10 (2) ◽  
pp. 291
Author(s):  
Zuherman Rustam ◽  
Aldi Purwanto ◽  
Sri Hartini ◽  
Glori Stephani Saragih
Keyword(s):  
Lung Cancer ◽  
Ct Scan ◽  
Lung Nodule ◽  
Image Data ◽  
Cluster Center ◽  
Support Vector ◽  
Fuzzy C Means ◽  
Kernel Distance ◽  
Abnormal Cells ◽  
Membership Matrix

<span id="docs-internal-guid-94842888-7fff-2ae1-cd5c-026943b95b7f"><span>Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy. </span></span>

Urban Noise Inference Model Based on Multiple Views and Kernel Tensor Decomposition

2021 ◽  
pp. 2150027
Author(s):  
Junlan Nie ◽  
Ruibo Gao ◽  
Ye Kang
Keyword(s):  
Noise Pollution ◽  
Tensor Decomposition ◽  
Prediction Algorithm ◽  
Kernel Matrix ◽  
Multiple Views ◽  
Tensor Factorization ◽  
Urban Noise ◽  
Model Based ◽  
Factorization Model ◽  
Kernel Distance

Prediction of urban noise is becoming more significant for tackling noise pollution and protecting human mental health. However, the existing noise prediction algorithms neglected not only the correlation between noise regions, but also the nonlinearity and sparsity of the data, which resulted in low accuracy of filling in the missing entries of data. In this paper, we propose a model based on multiple views and kernel-matrix tensor decomposition to predict the noise situation at different times of day in each region. We first construct a kernel tensor decomposition model by using kernel mapping in order to speed decomposition rate and realize stable estimate the prediction system. Then, we analyze and compute the cause of the noise from multiple views including computing the similarity of regions and the correlation between noise categories by kernel distance, which improves the credibility to infer the noise situation and the categories of regions. Finally, we devise a prediction algorithm based on the kernel-matrix tensor factorization model. We evaluate our method with a real dataset, and the experiments to verify the advantages of our method compared with other existing baselines.

scholarly journals Variational level set image segmentation model coupled with kernel distance function

2020 ◽  
Vol 14 ◽  
pp. 174830262093142 ◽  
Author(s):  
Noor Badshah ◽  
Ali Ahmad ◽  
Fazli Rehman
Keyword(s):  
Image Segmentation ◽  
Euclidean Distance ◽  
Complex Structure ◽  
Intensity Inhomogeneity ◽  
Distance Metrics ◽  
Proposed Model ◽  
Severe Intensity ◽  
Kernel Distance ◽  
Variational Level Set ◽  
Better Than

One of the crucial challenges in the area of image segmentation is intensity inhomogeneity. For most of the region-based models, it is not easy to completely segment images having severe intensity inhomogeneity and complex structure, as they rely on intensity distributions. In this work, we proposed a firsthand hybrid model by blending kernel and Euclidean distance metrics. Experimental results on some real and synthetic images suggest that our proposed model is better than models of Chan and Vese, Wu and He, and Salah et al.

scholarly journals Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics

Genes ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 298 ◽  
Author(s):  
Dunbar ◽  
Xu ◽  
Ryu ◽  
Ghosh ◽  
Shi ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Complex Traits ◽  
Scan Statistics ◽  
Type I ◽  
Scan Statistic ◽  
Differentially Methylated Regions ◽  
Distance Method ◽  
Cpg Sites ◽  
Kernel Distance ◽  
Statistic Approach

Motivation: Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases. Results: We propose two different approaches to test for differentially methylated regions (DMRs) associated with complex traits, while accounting for correlations among CpG sites in the DMRs. The first approach is a nonparametric method using a kernel distance statistic and the second one is a likelihood-based method using a binomial spatial scan statistic. The kernel distance method uses the kernel function, while the binomial scan statistic approach uses a mixed-effects model to incorporate correlations among CpG sites. Extensive simulations show that both approaches have excellent control of type I error, and both have reasonable statistical power. The binomial scan statistic approach appears to have higher power, while the kernel distance method is computationally faster. The proposed methods are demonstrated using data from a chronic lymphocytic leukemia (CLL) study.

scholarly journals A Kernel-Based Metric for Balance Assessment

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Yeying Zhu ◽  
Jennifer S. Savage ◽  
Debashis Ghosh
Keyword(s):  
Reproducing Kernel ◽  
Causal Effect ◽  
Distance Measures ◽  
Multivariate Distributions ◽  
Treatment Groups ◽  
Dieting Behavior ◽  
Kernel Distance ◽  
Kernel Hilbert Spaces ◽  
Matching Procedure ◽  
The Difference

AbstractAn important goal in causal inference is to achieve balance in the covariates among the treatment groups. In this article, we introduce the concept of distributional balance preserving which requires the distribution of the covariates to be the same in different treatment groups. We also introduce a new balance measure called kernel distance, which is the empirical estimate of the probability metric defined in the reproducing kernel Hilbert spaces. Compared to the traditional balance metrics, the kernel distance measures the difference in the two multivariate distributions instead of the difference in the finite moments of the distributions. Simulation results show that the kernel distance is the best indicator of bias in the estimated casual effect compared to several commonly used balance measures. We then incorporate kernel distance into genetic matching, the state-of-the-art matching procedure and apply the proposed approach to analyze the Early Dieting in Girls study. The study indicates that mothers’ overall weight concern increases the likelihood of daughters’ early dieting behavior, but the causal effect is not significant.

scholarly journals A Semi-Supervised Metric Learning for Content-Based Image Retrieval

2011 ◽  
Vol 1 (3) ◽  
pp. 53-63 ◽  
Author(s):  
I. Daoudi ◽  
K. Idrissi
Keyword(s):  
Probability Distributions ◽  
Metric Learning ◽  
Feature Space ◽  
Global Constraints ◽  
Learning Methods ◽  
Class A ◽  
Kernel Distance ◽  
New Space ◽  
Class Probability ◽  
Original Feature

In this paper, the authors propose a kernel-based approach to improve the retrieval performances of CBIR systems by learning a distance metric based on class probability distributions. Unlike other metric learning methods which are based on local or global constraints, the proposed method learns for each class a nonlinear kernel which transforms the original feature space to a more effective one. The distances between query and database images are then measured in the new space. Experimental results show that the kernel-based approach not only improves the retrieval performances of kernel distance without learning, but also outperforms other kernel metric learning methods.

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