Titlebook: Unsupervised Feature Extraction Applied to Bioinformatics; A PCA Based and TD B Y-h. Taguchi Book 20201st edition Springer Nature Switzerla

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書目名稱Unsupervised Feature Extraction Applied to Bioinformatics被引頻次




書目名稱Unsupervised Feature Extraction Applied to Bioinformatics被引頻次學科排名




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書目名稱Unsupervised Feature Extraction Applied to Bioinformatics年度引用學科排名




書目名稱Unsupervised Feature Extraction Applied to Bioinformatics讀者反饋




書目名稱Unsupervised Feature Extraction Applied to Bioinformatics讀者反饋學科排名

發(fā)出眩目光芒

Applications of PCA Based Unsupervised FE to BioinformaticsPCA based unsupervised FE ranges from biomarker identification and identification of disease causing genes to in silico drug discovery. I try to mention studies where PCA based unsupervised FE is applied as many as possible, from the published papers by myself.

conformity

2522-848X g data.Includes several applications to multi-view data anal.This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have the

喪失

Matrix Factorization matrices used to represent the original matrix by multiplication are small enough (i.e., lower rank), it can be considered to be reduction of degrees of freedom. Even if the matrix cannot be exactly represented as a product of two lower rank matrices, if it is possible for the product of matrices w

MELON

Tensor Decompositionf matrices are considered. In contrast to the MF that is usually represented as a product of two matrices, TD has various forms. In contrast to the matrices that were extensively studied over long period, tensor has much shorter history of extensive investigations, especially from the application po

brachial-plexus

PCA Based Unsupervised FEecially when the number of features attributed to individual samples is too huge to interpret. Mathematically, PCA is nothing but a linear projection of objects in high dimensional space onto low dimensional space. Alternatively, PC can be considered to be a tool that performs feature extraction (FE

不怕任性

TD Based Unsupervised FEledge, e.g., class labeling and period. In this chapter, I introduce TD based unsupervised FE as a natural extension of PCA based unsupervised FE towards tensors. In contrast to PCA that can deal with only one feature, TD can deal with multiple features, e.g., gene expression and miRNA expression si

GRUEL

安心地散步

keloid

Book 20201st editionervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra