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Local Context Finder (LCF) reveals multidimensional relationships among mRNA expression profiles of Arabidopsis responding to pathogen infection

F. Katagiri, J. Glazebrook
2003 Proceedings of the National Academy of Sciences of the United States of America  
A pattern recognition method called Local Context Finder (LCF) is described here. LCF uses nonlinear dimensionality reduction for pattern recognition.  ...  This is generally achieved by pattern recognition in the distribution of data points representing each profile in a high-dimensional space.  ...  (7) and Roweis and Saul (8) developed two different algorithms, Isomap and Locally Linear Embedding (LLE), respectively, to perform nonlinear dimensionality reduction for the purpose of pattern recognition  ... 
doi:10.1073/pnas.1934349100 pmid:12960373 pmcid:PMC196890 fatcat:ikhzdue57naxram6z5o2tnb3au

Dimensionality Reduction Oriented Toward the Feature Visualization for Ischemia Detection

E. Delgado-Trejos, A. Perera-Lluna, M. Vallverdu-Ferrer, P. Caminal-Magrans, G. Castellanos-Dominguez
2009 IEEE Transactions on Information Technology in Biomedicine  
set is projected to a 2-D space in order to verify the performance of the suggested dimension reduction algorithm in terms of the discrimination capability for ischemia detection.  ...  Next, an algorithm of variable selection is provided that further reduces the dimension, taking into account only the variables that offer greater class separability, and finally, the selected feature  ...  It can be noted that the observations for each one of the classes have a geometric disposition that makes pattern separability difficult.  ... 
doi:10.1109/titb.2009.2016654 pmid:19304491 fatcat:3w2br4rjrbhrxdxdvk2coypvqi

Measuring group-separability in geometrical space for evaluation of pattern recognition and embedding algorithms [article]

A. Acevedo, S. Ciucci, MJ. Kuo, C. Duran, CV. Cannistraci
2019 arXiv   pre-print
Evaluating data separation in a geometrical space is fundamental for pattern recognition.  ...  A plethora of dimensionality reduction (DR) algorithms have been developed in order to reveal the emergence of geometrical patterns in a low dimensional visible representation space, in which high-dimensional  ...  Group separability aims to evaluate in a geometrical space the extent to which groups (classes or populations) that are partially overlapping [21] , [22] , and display a pattern of separation between  ... 
arXiv:1912.12418v1 fatcat:wpcivrwpsjhcfnir36bidvvnk4

Dimensionality Reduction by Locally Linear Discriminant Analysis for Handwritten Chinese Character Recognition

Xue GAO, Jinzhi GUO, Lianwen JIN
2012 IEICE transactions on information and systems  
Linear Discriminant Analysis (LDA) is one of the most popular dimensionality reduction techniques in existing handwritten Chinese character (HCC) recognition systems.  ...  A series of experiments on both the HCL2000 and CASIA Chinese character handwriting databases show that our method can effectively improve recognition performance, with a reduction in error rate of 28.7%  ...  Acknowledgments This work is supported in part by NSFC (no. U0735004, 60772116, 61075021, 60902087) and GDSFC (no. S2011020000541, 2010B090400397, 2011B090400146).  ... 
doi:10.1587/transinf.e95.d.2533 fatcat:mhlqmcafpzbzndrvpfb3whlg2i

Review of Printed Fabric Pattern Segmentation Analysis and Application

Charles Kumah, Rafiu King Raji, Ruru Pan
2019 Autex Research Journal  
Several robust and efficient segmentation algorithms are established for pattern recognition.  ...  Image processing of digital images is one of the essential categories of image transformation in the theory and practice of digital pattern analysis and computer vision.  ...  Acknowledgments This work was financially supported by the Fundamental Research Funds for the Central Universities (JUSRP 41804).  ... 
doi:10.2478/aut-2019-0049 fatcat:4mvncw7jqraqnn2ff5ynjawd5a

Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments

Omar Lopez-Rincon, Oleg Starostenko, Vicente Alarcon-Aquino, Juan C. Galan-Hernandez
2017 Journal of Electrical and Computer Engineering  
The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that  ...  The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise.  ...  of multiple simple geometric shapes over a fixed space.  ... 
doi:10.1155/2017/4613628 fatcat:un4j6pneoncizokob32s4mrhc4

Machine Learning Approaches: From Theory to Application in Schizophrenia

Elisa Veronese, Umberto Castellani, Denis Peruzzo, Marcella Bellani, Paolo Brambilla
2013 Computational and Mathematical Methods in Medicine  
First we give a description of the basic terminology used in pattern recognition and machine learning.  ...  In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results  ...  Acknowledgments This study was partially supported by grants from the Italian Ministry of Health to Dr. Paolo Brambilla (GR-2010-2316745) and to Dr. Marcella Bellani (GR-2010-2319022).  ... 
doi:10.1155/2013/867924 pmid:24489603 pmcid:PMC3893837 fatcat:mhjhzq6ly5c2poqh6thznr7dyi

Feature Extraction Trends for Intelligent Facial Expression Recognition: A Survey

Irfan Azam, Sajid Ali Khan
2018 Informatica (Ljubljana, Tiskana izd.)  
Face detection, feature extraction and expression classification are the three main stages in the process of Facial Expression Recognition (FER).  ...  In human-machine interaction facial expression recognition plays a vital role. Still facial expression recognition through machines like computer is a difficult task.  ...  rate higher than 97% • Dimension reduction, • Similar recognition performance obtained in both using YUV color space and RGB color space • ROI extraction is accurate even if the brightness changes  ... 
doi:10.31449/inf.v42i4.2037 fatcat:zcmaj5oesneklfhf3ask7qbxsa

Computational intelligence techniques to detect toxic gas presence

Cesare Alippi, Gerardo Pelosi, Manuel Roveri
2006 2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications  
The aim of this paper is to critically discuss the use of a-priori knowledge in the design of gas sensor systems implementing computational intelligence techniques for signal processing and gas presence  ...  The availability of a-priori information about the probability density function of the considered classes as well as about the class separation boundary (Bayes boundary) allow the classifier designer for  ...  Dario Narducci and Ing. Dario Cogliati. The present activity has been supported by Fondazione Cariplo, Italy, under project TARGET.  ... 
doi:10.1109/cimsa.2006.250745 fatcat:gtvgx7pfi5eafpgissybacfg5y

An OD Flow Clustering Method Based on Vector Constraints: A Case Study for Beijing Taxi Origin-Destination Data

Xiaogang Guo, Zhijie Xu, Jianqin Zhang, Jian Lu, Hao Zhang
2020 ISPRS International Journal of Geo-Information  
of high-dimensional similarity of OD flow and helps mining representative OD flow clusters in flow space.  ...  Origin-destination (OD) flow pattern mining is an important research method of urban dynamics, in which OD flow clustering analysis discovers the activity patterns of urban residents and mine the coupling  ...  Acknowledgments: The authors would like to thank the anonymous reviewers for their valuable comments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi9020128 fatcat:jhoo32ndxzewtnfo6dc3lt54om

Kernel Locality Preserving Symmetrical Weighted Fisher Discriminant Analysis based subspace approach for expression recognition

G.P. Hegde, M. Seetha, Nagaratna Hegde
2016 Engineering Science and Technology, an International Journal  
This paper mainly focuses on dimensional reduction of fused dataset of holistic and geometrical face features vectors by solving singularity problem of linear discriminant analysis and maximizing the Fisher  ...  Performance of proposed approach is evaluated and compared with state of art approaches.  ...  The ''kernel trick" [58] allows for the computation of algorithms in a kernel domain space without explicitly evaluating the mapping, as long as the algorithm can be expressed in terms of dot products  ... 
doi:10.1016/j.jestch.2016.03.005 fatcat:hpt3nlyxqrhbhmblmdzakiw5hm

Learning Non-Euclidean Representations with SPD Manifold for Myoelectric Pattern Recognition

Dezhen Xiong, Daohui Zhang, Xingang Zhao, Yaqi Chu, Yiwen Zhao
2022 IEEE transactions on neural systems and rehabilitation engineering  
It shows the effectiveness of the presented approach and contributes a new way for myoelectric pattern recognition.  ...  Traditionally, hand-crafted features are extracted from individual EMG channels and combined together for pattern recognition.  ...  After that, a tangent feature matrix is flattened and dimension reduced for pattern recognition.  ... 
doi:10.1109/tnsre.2022.3178384 pmid:35622796 fatcat:lsi2fiuaabdwligqe2ygo5nx6u

Performance study of dimensionality reduction methods for metrology of nonrigid mechanical parts

H. Radvar-Esfahlan, S.-A. Tahan
2013 International Journal of Metrology and Quality Engineering  
In this paper we will only present a systematic comparison of some well-known dimensionality reduction techniques in order to evaluate their accuracy and potential for non-rigid metrology.  ...  The geometric measurement of parts using a coordinate measuring machine (CMM) has been generally adapted to the advanced automotive and aerospace industries.  ...  Acknowledgements This research is partially supported by the National Sciences and Engineering Research Council (NSERC). We also appreciate the authors of different codes in NLDR methods.  ... 
doi:10.1051/ijmqe/2013051 fatcat:t4sej52dfbehddofyidlsuvkzi

Manifold Learning Algorithms Applied to Structural Damage ‎Classification

Jersson X. Leon-Medina, Maribel Anaya, Diego A. Tibaduiza, Francesc Pozo
2021 Journal of Applied and Computational Mechanics  
Results evaluated in an experimental setup showed that the best classification accuracy was 100% when the methodology uses isomap algorithm with a hyperparameter k of 170 and 8 dimensions as a feature  ...  A comparative study of four manifold learning algorithms was carried out to perform the dimensionality reduction process within a proposed methodology for damage classification in structural health monitoring  ...  The authors thank the Editor and the anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.22055/jacm.2020.33055.2139 doaj:c64a31504016409aa055c55c131b3a48 fatcat:s6j53rcy6ra5znrrfwy7n6xsuu

Appearance-based object recognition using optimal feature transforms

Joachim Hornegger, Heinrich Niemann, Robert Risack
2000 Pattern Recognition  
We summarize several methods for improving the recognition rates and pose estimation accuracy of existing algorithms for 3 D object recognition.  ...  the feature dimensions and to beat the curse of dimensionality.  ...  Murase and K. Nayar for the friendly admission to use their Software Library for Appearance Matching SLAM.  ... 
doi:10.1016/s0031-3203(99)00048-5 fatcat:n7pmltkysfgsbdz7ur4y642shy
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