scholarly journals Boosting Threshold Classifiers for High– Dimensional Data in Functional Genomics

2008 ◽  
pp. 147-156 ◽  
Author(s):  
Ludwig Lausser ◽  
Malte Buchholz ◽  
Hans A. Kestler
Keyword(s):  
Functional Genomics ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals Identification of Block-Structured Covariance Matrix on an Example of Metabolomic Data

Separations ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 205
Author(s):  
Adam Mieldzioc ◽  
Monika Mokrzycka ◽  
Aneta Sawikowska
Keyword(s):  
Functional Genomics ◽  
Covariance Matrix ◽  
Drought Resistance ◽  
High Dimensional Data ◽  
Positive Definiteness ◽  
High Dimensional ◽  
Standard Methods ◽  
Metabolomic Data ◽  
Nutritional Sciences ◽  
Block Structured

Modern investigation techniques (e.g., metabolomic, proteomic, lipidomic, genomic, transcriptomic, phenotypic), allow to collect high-dimensional data, where the number of observations is smaller than the number of features. In such cases, for statistical analyzing, standard methods cannot be applied or lead to ill-conditioned estimators of the covariance matrix. To analyze the data, we need an estimator of the covariance matrix with good properties (e.g., positive definiteness), and therefore covariance matrix identification is crucial. The paper presents an approach to determine the block-structured estimator of the covariance matrix based on an example of metabolomic data on the drought resistance of barley. This method can be used in many fields of science, e.g., in agriculture, medicine, food and nutritional sciences, toxicology, functional genomics and nutrigenomics.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional

Approximate Cluster Heat Maps of Large High-Dimensional Data

2018 ◽  
Author(s):  
Punit Rathore ◽  
James C. Bezdek ◽  
Dheeraj Kumar ◽  
Sutharshan Rajasegarar ◽  
Marimuthu Palaniswami
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Heat Maps

scholarly journals The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 19
Author(s):  
Hsiuying Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Conventional Method ◽  
High Dimensional Data ◽  
Audio Signal ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Signal Recognition ◽  
Bayes Method ◽  
Generalized Bayes

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.

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