Treffer: Order-Preserving Factor Analysis—Application to Longitudinal Gene Expression

Title:
Order-Preserving Factor Analysis—Application to Longitudinal Gene Expression
Authors:
TIBAU PUIG, Arnau, WIESEL, Ami, ZAAS, Aimee K, WOODS, Chris W, GINSBURG, Geoffrey S, FLEURY, Gilles, HERO, Alfred O
Source:
IEEE transactions on signal processing. 59(9):4447-4458
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2011.
Publication Year:
2011
Physical Description:
print, 31 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie de l'information, Information theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Représentation du signal. Analyse spectrale, Signal representation. Spectral analysis, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Algorithme, Algorithm, Algoritmo, Analyse factorielle, Factor analysis, Análisis factorial, Apprentissage, Learning, Aprendizaje, Classification non supervisée, Unsupervised classification, Clasificación no supervisada, Classification signal, Signal classification, Dictionnaire, Dictionaries, Diccionario, Défaut alignement, Alignment defect, Defecto alineación, Modèle linéaire, Linear model, Modelo lineal, Programmation convexe, Convex programming, Programación convexa, Programmation non convexe, Non convex programming, Programación no convexa, Série temporelle, Time series, Serie temporal, Temps retard, Delay time, Tiempo retardo, Traitement donnée, Data processing, Tratamiento datos, Traitement signal, Signal processing, Procesamiento señal, Dictionary learning, genomic signal processing, misaligned data processing, structured factor analysis
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States
E3S—Supélec Systems Sciences/Signal Processing and Electronic Systems Department, Supélec, France
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
Institute for Genome Sciences and Policy, Duke University, Durham, NC 27706, United States
Division of Infectious Diseases and International Health, Department of Medicine, Duke University School of Medicine, Durham, NC 27706, United States
ISSN:
1053-587X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=24592566
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.24592566
Database:
PASCAL Archive

Weitere Informationen

We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited only after some other factors are activated. Precedence-ordering arise in applications where variables are activated in a specific order, which is unknown. The proposed method is based on a linear model that accounts for each factor's inherent delays and relative order. We present an algorithm to fit the model in an unsupervised manner using techniques from convex and nonconvex optimization that enforce sparsity of the factor scores and consistent precedence-order of the factor loadings. We illustrate the order-preserving factor analysis (OPFA) method for the problem of extracting precedence-ordered factors from a longitudinal (time course) study of gene expression data.