scholarly journals Image Segmentation and Identification of Paired Antibodies in Breast Tissue

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jimmy C. Azar ◽  
Martin Simonsson ◽  
Ewert Bengtsson ◽  
Anders Hast
Keyword(s):  
Breast Tissue ◽  
Principal Component ◽  
Feature Space ◽  
Tissue Microarrays ◽  
Specific Protein ◽  
Blue Dye ◽  
Computationally Efficient ◽  
Automated Quantification ◽  
Human Protein Atlas ◽  
Low Dimensional

Comparing staining patterns of paired antibodies designed towards a specific protein but toward different epitopes of the protein provides quality control over the binding and the antibodies’ ability to identify the target protein correctly and exclusively. We present a method for automated quantification of immunostaining patterns for antibodies in breast tissue using the Human Protein Atlas database. In such tissue, dark brown dye 3,3′-diaminobenzidine is used as an antibody-specific stain whereas the blue dye hematoxylin is used as a counterstain. The proposed method is based on clustering and relative scaling of features following principal component analysis. Our method is able (1) to accurately segment and identify staining patterns and quantify the amount of staining and (2) to detect paired antibodies by correlating the segmentation results among different cases. Moreover, the method is simple, operating in a low-dimensional feature space, and computationally efficient which makes it suitable for high-throughput processing of tissue microarrays.

Out-of-sample data visualization using bi-kernel t-SNE

2020 ◽  
pp. 147387162097820
Author(s):  
Haili Zhang ◽  
Pu Wang ◽  
Xuejin Gao ◽  
Yongsheng Qi ◽  
Huihui Gao
Keyword(s):  
Data Visualization ◽  
Principal Component ◽  
Feature Space ◽  
Gaussian Kernel ◽  
Sample Data ◽  
Out Of Sample ◽  
Benchmark Datasets ◽  
The Difference ◽  
Low Dimensional ◽  
Effective Visualization

T-distributed stochastic neighbor embedding (t-SNE) is an effective visualization method. However, it is non-parametric and cannot be applied to steaming data or online scenarios. Although kernel t-SNE provides an explicit projection from a high-dimensional data space to a low-dimensional feature space, some outliers are not well projected. In this paper, bi-kernel t-SNE is proposed for out-of-sample data visualization. Gaussian kernel matrices of the input and feature spaces are used to approximate the explicit projection. Then principal component analysis is applied to reduce the dimensionality of the feature kernel matrix. Thus, the difference between inliers and outliers is revealed. And any new sample can be well mapped. The performance of the proposed method for out-of-sample projection is tested on several benchmark datasets by comparing it with other state-of-the-art algorithms.

scholarly journals EVALUATING UNIFORM MANIFOLD APPROXIMATION AND PROJECTION FOR DIMENSION REDUCTION AND VISUALIZATION OF POLINSAR FEATURES

2021 ◽  
Vol V-1-2021 ◽  
pp. 39-46
Author(s):  
S. Schmitz ◽  
U. Weidner ◽  
H. Hammer ◽  
A. Thiele
Keyword(s):  
Dimension Reduction ◽  
Visual Analysis ◽  
Principal Component ◽  
Feature Space ◽  
Decomposition Methods ◽  
High Dimensional ◽  
Feature Representations ◽  
Wide Range ◽  
Nonlinear Dimension ◽  
Low Dimensional

Abstract. In this paper, the nonlinear dimension reduction algorithm Uniform Manifold Approximation and Projection (UMAP) is investigated to visualize information contained in high dimensional feature representations of Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data. Based on polarimetric parameters, target decomposition methods and interferometric coherences a wide range of features is extracted that spans the high dimensional feature space. UMAP is applied to determine a representation of the data in 2D and 3D euclidean space, preserving local and global structures of the data and still suited for classification. The performance of UMAP in terms of generating expressive visualizations is evaluated on PolInSAR data acquired by the F-SAR sensor and compared to that of Principal Component Analysis (PCA), Laplacian Eigenmaps (LE) and t-distributed Stochastic Neighbor embedding (t-SNE). For this purpose, a visual analysis of 2D embeddings is performed. In addition, a quantitative analysis is provided for evaluating the preservation of information in low dimensional representations with respect to separability of different land cover classes. The results show that UMAP exceeds the capability of PCA and LE in these regards and is competitive with t-SNE.

scholarly journals Hyperspectral Image Classification Based on Two-Stage Subspace Projection

2018 ◽  
Vol 10 (10) ◽  
pp. 1565 ◽  
Author(s):  
Xiaoyan Li ◽  
Lefei Zhang ◽  
Jane You
Keyword(s):  
Hyperspectral Image ◽  
Small Neighborhood ◽  
Principal Component ◽  
Feature Space ◽  
Kernel Principal Component Analysis ◽  
Subspace Projection ◽  
Two Stage ◽  
Discrimination Information ◽  
Highly Correlated ◽  
Low Dimensional

Hyperspectral image (HSI) classification is a widely used application to provide important information of land covers. Each pixel of an HSI has hundreds of spectral bands, which are often considered as features. However, some features are highly correlated and nonlinear. To address these problems, we propose a new discrimination analysis framework for HSI classification based on the Two-stage Subspace Projection (TwoSP) in this paper. First, the proposed framework projects the original feature data into a higher-dimensional feature subspace by exploiting the kernel principal component analysis (KPCA). Then, a novel discrimination-information based locality preserving projection (DLPP) method is applied to the preceding KPCA feature data. Finally, an optimal low-dimensional feature space is constructed for the subsequent HSI classification. The main contributions of the proposed TwoSP method are twofold: (1) the discrimination information is utilized to minimize the within-class distance in a small neighborhood, and (2) the subspace found by TwoSP separates the samples more than they would be if DLPP was directly applied to the original HSI data. Experimental results on two real-world HSI datasets demonstrate the effectiveness of the proposed TwoSP method in terms of classification accuracy.

A LIGHTWEIGHT SYSTEM TOWARDS VIEWING ANGLE AND CLOTHING VARIATION IN GAIT RECOGNITION

2021 ◽  
Vol 2 (1) ◽  
pp. 0-0
Keyword(s):  
Principal Component Analysis ◽  
Gait Recognition ◽  
Principal Component ◽  
Superior Performance ◽  
Multi Layer Perceptron ◽  
Viewing Angle ◽  
Computationally Efficient ◽  
Angle Variation ◽  
Second Stage ◽  
Low Dimensional

In this paper, the authors have proposed a computationally efficient, robust, and lightweight system for gait recognition. The proposed system contains two main stages: In the first stage, a classification network identifies optical flow corners in the normalized silhouette and calculates the distances traveled in every viewpoint which is further used by a regression model to identify the viewing angle. In the second stage, a feature extraction network computes the Gait Energy Image (GEI) for every viewpoint and then uses Principal Component Analysis (PCA) to extract low dimensional feature vectors from these GEI images. Finally, a multi-layer perceptron model is trained using the extracted principal components for every viewing angle. The performance of a system is comprehensively evaluated on the CASIA B and OULP gait dataset. The experimental results demonstrate the superior performance of a proposed system in viewing angle classification (100% accuracy), gait recognition (100% accuracy in normal walk), computational efficiency, robustness to clothing, and viewing angle variation.

Anatomization of the systems of dimension relaxation for facial recognition

2020 ◽  
pp. 1-11
Author(s):  
Mayamin Hamid Raha ◽  
Tonmoay Deb ◽  
Mahieyin Rahmun ◽  
Tim Chen
Keyword(s):  
Face Recognition ◽  
Main Idea ◽  
Principal Component ◽  
Feature Space ◽  
Projection Methods ◽  
Data Representation ◽  
Linear Discriminant ◽  
Class Differences ◽  
Underlying Space ◽  
Maximum Variance

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.

scholarly journals Numerical Valuation of American Basket Options via Partial Differential Complementarity Problems

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1498
Author(s):  
Karel J. in’t Hout ◽  
Jacob Snoeijer
Keyword(s):  
Principal Component Analysis ◽  
Numerical Experiments ◽  
Complementarity Problems ◽  
Principal Component ◽  
Partial Differential ◽  
Basket Options ◽  
Basket Option ◽  
Option Values ◽  
Convergence Behaviour ◽  
Low Dimensional

We study the principal component analysis based approach introduced by Reisinger and Wittum (2007) and the comonotonic approach considered by Hanbali and Linders (2019) for the approximation of American basket option values via multidimensional partial differential complementarity problems (PDCPs). Both approximation approaches require the solution of just a limited number of low-dimensional PDCPs. It is demonstrated by ample numerical experiments that they define approximations that lie close to each other. Next, an efficient discretisation of the pertinent PDCPs is presented that leads to a favourable convergence behaviour.

scholarly journals Optimizing the structure and movement of a robotic bat with biological kinematic synergies

2018 ◽  
Vol 37 (10) ◽  
pp. 1233-1252 ◽  
Author(s):  
Jonathan Hoff ◽  
Alireza Ramezani ◽  
Soon-Jo Chung ◽  
Seth Hutchinson
Keyword(s):  
Principal Component Analysis ◽  
Topological Structure ◽  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Principal Component ◽  
Biologically Inspired ◽  
Wing Kinematics ◽  
Wing Motion ◽  
Kinematic Synergies ◽  
Low Dimensional

In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot’s parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold.

scholarly journals c-Met overexpression in inflammatory breast carcinomas: automated quantification on tissue microarrays

2007 ◽  
Vol 96 (2) ◽  
pp. 329-335 ◽  
Author(s):  
S Garcia ◽  
J-P Dalès ◽  
J Jacquemier ◽  
E Charafe-Jauffret ◽  
D Birnbaum ◽  
...  
Keyword(s):  
Tissue Microarrays ◽  
Breast Carcinomas ◽  
Automated Quantification
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