Feature Extraction from NMR Images Using Factor Analysis

1990 ◽  
pp. 161-164 ◽  
Author(s):  
M. Šámal ◽  
M. Kárný ◽  
H. Sůrová ◽  
J. Zajíček
Keyword(s):  
Factor Analysis ◽  
Feature Extraction

Feature Extraction of Robot Sensor Data Using Factor Analysis for Behavior Learning

2004 ◽  
Vol 8 (3) ◽  
pp. 284-294
Author(s):  
Wai-keung Fung ◽  
◽  
Yun-hui Liu
Keyword(s):  
Factor Analysis ◽  
Feature Extraction ◽  
Sensor Data ◽  
Range Data ◽  
Perceptual Space ◽  
Latent Factors ◽  
New Approach ◽  
Robot Behavior ◽  
Behavior Learning ◽  
Sensor Information

The paper addresses feature extraction of sensor data for robot behavior learning using factor analysis. Redundancies in sensor types and quantities are common in sensing competence of robots. The redundancies cause the high dimensionality of the perceptual space. It is impractical to incorporate all available sensor information in decision-making and learning of robots due to the huge memory and computational requirements. This paper proposes a new approach to extract important knowledge from sensor data based on the inter-correlation of sensor data using factor analysis and construct logical perceptual space for robot behavior learning. The logical perceptual space is constructed by hypothetical latent factors extracted using factor analysis. Since the latent factors extracted have fewer dimensions than raw sensor data, using the logical perceptual space in behavior learning would significantly simplify the learning process and architecture. Experiments have been conducted to demonstrate the process of logical perceptual space extraction from ultrasonic range data for robot behavior learning.

FACTOR ANALYSIS FOR CROSS-PLATFORM TUMOR CLASSIFICATION BASED ON GENE EXPRESSION PROFILES

2010 ◽  
Vol 19 (01) ◽  
pp. 243-258 ◽  
Author(s):  
SHU-LIN WANG ◽  
JIE GUI ◽  
XUELING LI
Keyword(s):  
Gene Expression ◽  
Factor Analysis ◽  
Feature Extraction ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tumor Classification ◽  
Classification Model ◽  
Latent Factors ◽  
Cross Platform

Previous studies on tumor classification based on feature extraction from gene expression profiles (GEP) were proven to be effective, but some of such methods lack biomedical meaning to some extent. To deal with this problem, we proposed a novel feature extraction method whose experimental results are of biomedical interpretability and helpful for gaining insight into the structure analysis of gene expression dataset. This method first applied rank sum test to roughly select a set of informative genes and then adopted factor analysis to extract latent factors for tumor classification. Experiments on three pairs of cross-platform tumor datasets indicated that the proposed method can obviously improve the performance of cross-platform classification and only several latent factors, which can represent a large number of informative genes, would obtain very high predictive accuracy on test set. The results also suggested that the classification model trained on one dataset can successfully predict another tumor dataset with the same tumor subtype obtained on different experimental platforms.

scholarly journals A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Ningbo Hao ◽  
Jie Yang ◽  
Haibin Liao ◽  
Wenhua Dai
Keyword(s):  
Factor Analysis ◽  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Mapping Function ◽  
Classification Performance ◽  
Analysis Framework ◽  
Linear Discriminant ◽  
Discriminative Feature ◽  
Common Framework ◽  
Content Factor

Various methods for feature extraction and dimensionality reduction have been proposed in recent decades, including supervised and unsupervised methods and linear and nonlinear methods. Despite the different motivations of these methods, we present in this paper a general formulation known as factor analysis to unify them within a common framework. During factor analysis, an object can be seen as being comprised of content and style factors, and the objective of feature extraction and dimensionality reduction is to obtain the content factor without style factor. There are two vital steps in factor analysis framework; one is the design of factor separating objective function, including the design of partition and weight matrix, and the other is the design of space mapping function. In this paper, classical Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP) algorithms are improved based on factor analysis framework, and LDA based on factor analysis (FA-LDA) and LPP based on factor analysis (FA-LPP) are proposed. Experimental results show the superiority of our proposed approach in classification performance compared to classical LDA and LPP algorithms.

scholarly journals A Feature Extraction Technique Based on Factor Analysis for Pulsed Eddy Current Defects Categorization

2019 ◽  
Vol 554 ◽  
pp. 012001
Author(s):  
Nurul A’in Ahmad Latif ◽  
Ilham Mukriz Zainal Abidin ◽  
Noorhazleena Azaman ◽  
Nordin Jamaludin ◽  
Ainul Akmar Mokhtar
Keyword(s):  
Factor Analysis ◽  
Feature Extraction ◽  
Eddy Current ◽  
Extraction Technique ◽  
Pulsed Eddy Current ◽  
Feature Extraction Technique

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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