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.

scholarly journals EVALUATION OF MULTIPLE RETAILERS’ MARKET POSITIONS ON THE BASIS OF IMAGE ATTRIBUTES MEASUREMENT

2005 ◽  
Vol 6 (4) ◽  
pp. 199-206 ◽  
Author(s):  
Sigitas Urbonavičius ◽  
Robertas Ivanauskas
Keyword(s):  
Factor Analysis ◽  
Price Competition ◽  
Perceived Value ◽  
Image Management ◽  
Perceptual Space ◽  
Latent Factors ◽  
Intense Competition ◽  
Customer Perceived Value ◽  
Retail Company

Intense competition in retailing sector requires searching for new and more effective tools of competing with rivals. One of the possible ways seems to go through applying positioning concept in retailing. Positioning in retailing refers to strategy for development of a desirable image, which would help to differentiate a retail company and move away from direct price competition. Besides that, image management provides possibilities for increasing customer perceived value and/or increasing prices. The paper presents methodology for establishing multiple retailers’ positions. This methodology is based on evaluation of image attributes’ importance for customers. Factor analysis allows revealing more general latent factors that are used to evaluate retailers’ positions in a perceptual space. This allows drawing conclusions on how much Lithuanian multiple retailers are similar or differentiated from the standpoint of their customers.

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.

The Cross-Cultural Equivalence of Parental Rearing Measure: EMBU-C

2006 ◽  
Vol 22 (2) ◽  
pp. 85-91 ◽  
Author(s):  
Maja Deković ◽  
Margreet ten Have ◽  
Wilma A.M. Vollebergh ◽  
Trees Pels ◽  
Annerieke Oosterwegel ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Cultural Differences ◽  
Cross Cultural ◽  
Child Rearing ◽  
Latent Factors ◽  
Immigrant Adolescents ◽  
Confirmatory Factor ◽  
Parental Rearing ◽  
The Cross ◽  
Multigroup Confirmatory Factor Analysis

We examined the cross-cultural equivalence of a widely used instrument that assesses perceived parental rearing, the EMBU-C, among native Dutch and immigrant adolescents living in The Netherlands. The results of a multigroup confirmatory factor analysis indicated that the factor structure of the EMBU-C, consisting of three latent factors (Warmth, Rejection, and Overprotection), and reliabilities of these scales are similar in both samples. These findings lend further support for the factorial and construct validity of this instrument. The comparison of perceived child rearing between native Dutch and immigrant adolescents showed cultural differences in only one of the assessed dimensions: Immigrant adolescents perceive their parents as more overprotective than do Dutch adolescents.

scholarly journals Bayesian cumulative shrinkage for infinite factorizations

Biometrika ◽  
2020 ◽  
Vol 107 (3) ◽  
pp. 745-752 ◽  
Author(s):  
Sirio Legramanti ◽  
Daniele Durante ◽  
David B Dunson
Keyword(s):  
Monte Carlo ◽  
Factor Analysis ◽  
Monte Carlo Algorithm ◽  
Model Complexity ◽  
Latent Factors ◽  
Shrinkage Process ◽  
Shrinkage Prior ◽  
Performance Gains ◽  
Shrinkage Priors ◽  
Model Dimension

Summary The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approach that progressively penalizes expansions with growing complexity. In this article we propose a novel increasing shrinkage prior, called the cumulative shrinkage process, for the parameters that control the dimension in overcomplete formulations. Our construction has broad applicability and is based on an interpretable sequence of spike-and-slab distributions which assign increasing mass to the spike as the model complexity grows. Using factor analysis as an illustrative example, we show that this formulation has theoretical and practical advantages relative to current competitors, including an improved ability to recover the model dimension. An adaptive Markov chain Monte Carlo algorithm is proposed, and the performance gains are outlined in simulations and in an application to personality data.

scholarly journals The Importance of Service Attributes between Low-cost and Full-service Carrier Customers: A case of Airline Type Hybridisation

2021 ◽  
Vol 10 (10(6)) ◽  
pp. 1741-1757
Author(s):  
Nkululeko Funyane
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Exploratory Factor Analysis ◽  
Low Cost ◽  
Latent Factors ◽  
Significant Difference ◽  
The Difference

This study sought to assess if the importance attached by customers to the airline service attributes differed across low-cost and full-service airline models. A Mann-Whitney U Test was used to assess the difference between the two models. However, before subjecting the data to differential tests, an exploratory factor analysis (maximum likelihood) was performed on the fifty-five items of service attributes, reducing them into forty-two items retained into ten latent factors (airline service attributes). The results of the revealed a significant difference in the importance attached to staff competence, courtesy and responsiveness only. Such findings suggest that the positioning of airlines into binary (FSC - LCC) models could be a waste of effort and resources since airlines seem to be converging.

Sign in / Sign up

Export Citation Format

Share Document