Using dimension reduction with feature selection to enhance accuracy of tumor classification

2016 ◽  
Author(s):  
Thuy Hang Dang ◽  
Dung Pham Trung ◽  
Hoai Linh Tran ◽  
Quang Le Van
Keyword(s):  
Feature Selection ◽  
Dimension Reduction ◽  
Tumor Classification

scholarly journals Dimension Reduction for Objects Composed of Vector Sets

2017 ◽  
Vol 27 (1) ◽  
pp. 169-180 ◽  
Author(s):  
Marton Szemenyei ◽  
Ferenc Vajda
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Discriminant Analysis ◽  
Probability Distribution ◽  
Dimension Reduction ◽  
Pose Estimation ◽  
Real World ◽  
Single Object ◽  
Real World Datasets

Abstract Dimension reduction and feature selection are fundamental tools for machine learning and data mining. Most existing methods, however, assume that objects are represented by a single vectorial descriptor. In reality, some description methods assign unordered sets or graphs of vectors to a single object, where each vector is assumed to have the same number of dimensions, but is drawn from a different probability distribution. Moreover, some applications (such as pose estimation) may require the recognition of individual vectors (nodes) of an object. In such cases it is essential that the nodes within a single object remain distinguishable after dimension reduction. In this paper we propose new discriminant analysis methods that are able to satisfy two criteria at the same time: separating between classes and between the nodes of an object instance. We analyze and evaluate our methods on several different synthetic and real-world datasets.

scholarly journals Author Correction: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
F. William Townes ◽  
Stephanie C. Hicks ◽  
Martin J. Aryee ◽  
Rafael A. Irizarry
Keyword(s):  
Feature Selection ◽  
Dimension Reduction ◽  
Single Cell ◽  
Multinomial Model ◽  
Rna Seq

scholarly journals Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
F. William Townes ◽  
Stephanie C. Hicks ◽  
Martin J. Aryee ◽  
Rafael A. Irizarry
Keyword(s):  
Feature Selection ◽  
Dimension Reduction ◽  
Single Cell ◽  
Current Practice ◽  
Principal Component ◽  
Ground Truth ◽  
Rna Seq ◽  
Normal Distributions ◽  
Multinomial Sampling ◽  
Negative Controls

AbstractSingle-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.

Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering

2017 ◽  
Vol 84 ◽  
pp. 24-36 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Osama Ahmad Alomari
Keyword(s):  
Feature Selection ◽  
Dimension Reduction ◽  
Document Clustering ◽  
Text Document ◽  
Text Feature ◽  
Dynamic Dimension

Feature Selection and Dimension Reduction of Social Autism Data

2019 ◽  
Author(s):  
Peter Washington ◽  
Kelley Marie Paskov ◽  
Haik Kalantarian ◽  
Nathaniel Stockham ◽  
Catalin Voss ◽  
...  
Keyword(s):  
Feature Selection ◽  
Dimension Reduction
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