Unsupervised clustering method for pattern recognition in IIF images

2016 ◽  
Author(s):  
Letizia Vivona ◽  
Donato Cascio ◽  
Salvatore Bruno ◽  
Alessandro Fauci ◽  
Vincenzo Taormina ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Unsupervised Clustering ◽  
Clustering Method

scholarly journals Dependence-Maximization Clustering with Least-Squares Mutual Information

2011 ◽  
Vol 15 (7) ◽  
pp. 800-805 ◽  
Author(s):  
Manabu Kimura ◽  
◽  
Masashi Sugiyama
Keyword(s):  
Mutual Information ◽  
Least Squares ◽  
Cross Validation ◽  
Unsupervised Clustering ◽  
Statistical Dependence ◽  
Clustering Method ◽  
Regularization Parameters ◽  
Loss Variant ◽  
Dependence Measures ◽  
Kernel Parameters

Recently, statistical dependence measures such as mutual information and kernelized covariance have been successfully applied to clustering. In this paper, we follow this line of research and propose a novel dependence-maximization clustering method based on least-squares mutual information, which is an estimator of a squared-loss variant of mutual information. A notable advantage of the proposed method over existing approaches is that hyperparameters such as kernel parameters and regularization parameters can be objectively optimized based on cross-validation. Thus, subjective manual-tuning of hyperparameters is not necessary in the proposed method, which is a highly useful property in unsupervised clustering scenarios. Through experiments, we illustrate the usefulness of the proposed approach.

scholarly journals Fiber_distance based unsupervised clustering method of MR tractography data

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S524
Author(s):  
Sang-Han Choi ◽  
Young-Bo Kim ◽  
Zang-Hee Cho
Keyword(s):  
Unsupervised Clustering ◽  
Clustering Method ◽  
Tractography Data

scholarly journals Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method

2018 ◽  
Vol 12 (7) ◽  
pp. 989-995 ◽  
Author(s):  
Letizia Vivona ◽  
Donato Cascio ◽  
Vincenzo Taormina ◽  
Giuseppe Raso
Keyword(s):  
Indirect Immunofluorescence ◽  
Unsupervised Clustering ◽  
Clustering Method

scholarly journals The effect of linkages in the Hierarchical Clustering of Auto-Regressive algorithm for Defect Identification in Heat Exchanger Tubes

2019 ◽  
Vol 8 (4) ◽  
pp. 5003-5009
Keyword(s):  
Pattern Recognition ◽  
Heat Exchanger ◽  
Hierarchical Clustering ◽  
Stress Wave ◽  
Stress Waves ◽  
Clustering Method ◽  
Defect Identification ◽  
Auto Regressive ◽  
Pattern Recognition Approach ◽  
Maximum Group

Pattern recognition approach based on Auto-Regressive (AR) algorithm is an alternative way to provide a more accurate defect identification from stress wave propagated along ASTM A179 heat exchanger tubes. The AR algorithm characterizes the shape of the stress wave signals by AR coefficients and clustered using ‘centroid’ linkages. However, the increase of number of stress waves limiting the function of clustering into meaningful groups. This paper proposes the ‘ward’ linkages as an improved hierarchical clustering method to define the defect features from the reference tube signals and those from the artificially induced defective tubes. The clustering results from the ‘ward’ linkages were represented via a dendrogram showing the hidden pattern between clusters. The defect in the heat exchanger tubes are easily interpreted from the dendrogram and can be successfully identified from Maximum Group Distance Criteria (MGDC). The pattern recognition approach using ‘ward’ linkages in AR algorithm has been shown to effectively identify the defects in the heat exchanger tubes

scholarly journals Reduced serum levels of pro-inflammatory chemokines in Fragile X Syndrome

2020 ◽  
Author(s):  
Anke Van Dijck ◽  
Susana Barbosa ◽  
Patricia Bermudez-Martin ◽  
Olfa Khalfallah ◽  
Cyprien Gilet ◽  
...  
Keyword(s):  
Fragile X Syndrome ◽  
Robust Regression ◽  
Fragile X ◽  
Serum Levels ◽  
Unsupervised Clustering ◽  
Healthy Controls ◽  
Healthy Individuals ◽  
Clustering Method ◽  
Inflammatory Chemokines ◽  
Healthy Control

Abstract Background: Fragile X syndrome (FXS) is the most frequent cause of inherited intellectual disability and the most commonly identified monogenic cause of autism. Recent studies have shown that long-term pathological consequences of FXS are not solely confined to the central nervous system (CNS) but rather extend to other physiological dysfunctions in peripheral organs. To gain insights into possible immune dysfunctions in FXS, we profiled a large panel of immune-related biomarkers in the serum of FXS patients and healthy controls. Methods: We have used a sensitive and robust Electro Chemi Luminescence (ECL)-based immunoassay to measure the levels of 52 cytokines in the serum of n=25 FXS patients and n=29 healthy controls. We then used univariate statistics and multivariate analysis, as well as an advanced unsupervised clustering method, to identify combinations of immune-related biomarkers that could discriminate FXS patients from healthy individuals. Results: While the majority of the tested cytokines were present at similar levels in FXS patients and healthy individuals, nine chemokines, CCL2, CCL3, CCL4, CCL11, CCL13, CCL17, CCL22, CCL26 and CXCL10, were present at much lower levels in FXS patients. Using robust regression, we show that six of these biomarkers (CCL2, CCL3, CCL11, CCL22, CCL26 and CXCL10) were negatively associated with FXS diagnosis. Finally, applying the K-sparse unsupervised clustering method to the biomarker dataset allowed for the identification of two subsets of individuals, which essentially matched the FXS and healthy control categories. Conclusions: Our data show that FXS patients exhibit reduced serum levels of several chemokines. This paves the way for further study of immune phenotypes in FXS patients.

Methods of Semantic Integrity Preservation in the Pattern Recognition Process

2019 ◽  
Vol 10 (3) ◽  
pp. 118-140
Author(s):  
Iuliia Kim ◽  
Anastasiia Matveeva ◽  
Ilya Viksnin ◽  
Roman Patrikeev
Keyword(s):  
Pattern Recognition ◽  
Visual Information ◽  
Clustering Method ◽  
Swarm Optimization ◽  
Recognition Process ◽  
Second Stage ◽  
Initial Stage ◽  
Recognition Quality ◽  
Training Samples ◽  
Blurred Images

In this article, much attention is paid to pattern recognition quality, especially the visual information semantic integrity preservation. The main purpose is to find the ways of its possible improvement to the three basic stages of the pattern recognition process: image preparation, image processing, and classification. To avoid semantic integrity violations of information, in the initial stage of the image analysis, normalization is proposed. In the second stage, a new clustering method was developed, based on particle swarm optimization and the k-means algorithm. In the final stage of the pattern recognition process the Haar classifier was used with normalized training samples. The proposed algorithm and only Haar classifier with non-normalized samples were tested on 500 blurred images: in 8% of samples both algorithms provided semantic integrity preservation and in 64% only the developed algorithm worked effectively.

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