scholarly journals Weighted dimensionality reduction and robust Gaussian mixture model based cancer patient subtyping from gene expression data

2020 ◽  
Vol 112 ◽  
pp. 103620
Author(s):  
Omar Rafique ◽  
A.H. Mir
Keyword(s):  
Gene Expression ◽  
Cancer Patient ◽  
Dimensionality Reduction ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gene Expression Data ◽  
Gaussian Mixture ◽  
Expression Data ◽  
Model Based

scholarly journals A Constrained Gaussian Mixture Model for Correlation-Based Cluster Analysis of Gene Expression Data

2009 ◽  
Vol 2 ◽  
pp. 47-62
Author(s):  
Naoto Yukinawa ◽  
Taku Yoshioka ◽  
Kazuo Kobayashi ◽  
Naotake Ogasawara ◽  
Shin Ishii
Keyword(s):  
Gene Expression ◽  
Cluster Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gene Expression Data ◽  
Gaussian Mixture ◽  
Expression Data

A Gaussian mixture model based virtual sample generation approach for small datasets in industrial processes

2021 ◽  
Vol 581 ◽  
pp. 262-277
Author(s):  
Ling Li ◽  
Seshu Kumar Damarla ◽  
Yalin Wang ◽  
Biao Huang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Industrial Processes ◽  
Virtual Sample ◽  
Model Based

scholarly journals Massive MIMO Codebook Design Using Gaussian Mixture Model Based Clustering

2022 ◽  
Vol 32 (1) ◽  
pp. 361-375
Author(s):  
S. Markkandan ◽  
S. Sivasubramanian ◽  
Jaison Mulerikkal ◽  
Nazeer Shaik ◽  
Beulah Jackson ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Massive Mimo ◽  
Gaussian Mixture ◽  
Model Based Clustering ◽  
Codebook Design ◽  
Model Based

COMBINING GENERALIZED NMF AND DISCRIMINATIVE MIXTURE MODELS FOR CLASSIFICATION OF GENE EXPRESSION DATA

2008 ◽  
Vol 22 (08) ◽  
pp. 1587-1598 ◽  
Author(s):  
WEIXIANG LIU ◽  
KEHONG YUAN ◽  
JIAN WU ◽  
DATIAN YE ◽  
ZHEN JI ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mixture Model ◽  
Gene Expression Data ◽  
Small Sample Size ◽  
Data Classification ◽  
Small Sample ◽  
Training Data ◽  
Microarray Data Analysis ◽  
Expression Data

Classification of gene expression samples is a core task in microarray data analysis. How to reduce thousands of genes and to select a suitable classifier are two key issues for gene expression data classification. This paper introduces a framework on combining both feature extraction and classifier simultaneously. Considering the non-negativity, high dimensionality and small sample size, we apply a discriminative mixture model which is designed for non-negative gene express data classification via non-negative matrix factorization (NMF) for dimension reduction. In order to enhance the sparseness of training data for fast learning of the mixture model, a generalized NMF is also adopted. Experimental results on several real gene expression datasets show that the classification accuracy, stability and decision quality can be significantly improved by using the generalized method, and the proposed method can give better performance than some previous reported results on the same datasets.

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