Robust tensor principal component analysis by Lp-norm for image analysis

2016 ◽  
Author(s):  
Ganyi Tang ◽  
Guifu Lu ◽  
Zhongqun Wang ◽  
Yukai Xie
Keyword(s):  
Principal Component Analysis ◽  
Image Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Lp Norm

scholarly journals Joint Lp-Norm and L2,1-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiang-Zhen Kong ◽  
Yu Song ◽  
Jin-Xing Liu ◽  
Chun-Hou Zheng ◽  
Sha-Sha Yuan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Network ◽  
Principal Component ◽  
Graph Laplacian ◽  
Component Analysis ◽  
Network Module ◽  
Tumor Sample ◽  
Lp Norm ◽  
Module Discovery

The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph Laplacian principal component analysis (PL21GPCA) based on traditional principal component analysis (PCA) is proposed for robust tumor sample clustering and gene network module discovery. Three aspects are highlighted in the PL21GPCA method. First, to degrade the high sensitivity to outliers and noise, the non-convex proximal Lp-norm (0 < p < 1)constraint is applied on the loss function. Second, to enhance the sparsity of gene expression in cancer samples, the L2,1-norm constraint is used on one of the regularization terms. Third, to retain the geometric structure of the data, we introduce the graph Laplacian regularization item to the PL21GPCA optimization model. Extensive experiments on five gene expression datasets, including one benchmark dataset, two single-cancer datasets from The Cancer Genome Atlas (TCGA), and two integrated datasets of multiple cancers from TCGA, are performed to validate the effectiveness of our method. The experimental results demonstrate that the PL21GPCA method performs better than many other methods in terms of tumor sample clustering. Additionally, this method is used to discover the gene network modules for the purpose of finding key genes that may be associated with some cancers.

Comparing Ranking Methods for Tensor Components in Multilinear and Concurrent Subspace Analysis with Applications in Face Images

2015 ◽  
Vol 15 (01) ◽  
pp. 1550006 ◽  
Author(s):  
Tiene A. Filisbino ◽  
Gilson A. Giraldi ◽  
Carlos E. Thomaz
Keyword(s):  
Principal Component Analysis ◽  
Image Analysis ◽  
Gait Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Face Image ◽  
Image Databases ◽  
Subspace Analysis ◽  
Raw Data ◽  
Face Image Analysis

In the area of multi-dimensional image databases modeling, the multilinear principal component analysis (MPCA) and concurrent subspace analysis (CSA) approaches were independently proposed and applied for mining image databases. The former follows the classical principal component analysis (PCA) paradigm that centers the sample data before subspace learning. The CSA, on the other hand, performs the learning procedure using the raw data. Besides, the corresponding tensor components have been ranked in order to identify the principal tensor subspaces for separating sample groups for face image analysis and gait recognition. In this paper, we first demonstrate that if CSA receives centered input samples and we consider full projection matrices then the obtained solution is equal to the one generated by MPCA. Then, we consider the general problem of ranking tensor components. We examine the theoretical aspects of typical solutions in this field: (a) Estimating the covariance structure of the database; (b) Computing discriminant weights through separating hyperplanes; (c) Application of Fisher criterium. We discuss these solutions for tensor subspaces learned using centered data (MPCA) and raw data (CSA). In the experimental results we focus on tensor principal components selected by the mentioned techniques for face image analysis considering gender classification as well as reconstruction problems.

Influence and Optimization of Boiler Efficiency

2014 ◽  
Vol 707 ◽  
pp. 352-355
Author(s):  
Hao Li
Keyword(s):  
Principal Component Analysis ◽  
Image Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Excess Air ◽  
Boiler Efficiency ◽  
Excess Air Coefficient

The principles-related knowledge of boiler is mainly applied to analyze the relationships between boiler efficiency and excess air coefficient and the main parameters, and also the method of optimizing operation of the boiler is proposed.Key words: image analysis method; principal component analysis; boiler efficiency; optimizing operation

FTIR Microscopic Image Analysis Based on Principal Component Analysis-2nd Derivative Spectral Imaging

2012 ◽  
Vol 32 (7) ◽  
pp. 0711001
Author(s):  
杨秀坤 Yang Xiukun ◽  
钟明亮 Zhong Mingliang ◽  
景晓军 Jing Xiaojun ◽  
张尚迪 Zhang Shangdi
Keyword(s):  
Principal Component Analysis ◽  
Image Analysis ◽  
Spectral Imaging ◽  
Principal Component ◽  
Component Analysis ◽  
Microscopic Image ◽  
Microscopic Image Analysis
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