Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 3/2018

07.02.2017 | Theoretical Advances

A new linear discriminant analysis algorithm based on L1-norm maximization and locality preserving projection

verfasst von: Di Zhang, Xueqiang Li, Jiazhong He, Minghui Du

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Generic L2-norm-based linear discriminant analysis (LDA) is sensitive to outliers and only captures global structure information of sample points. In this paper, a new LDA-based feature extraction algorithm is proposed to integrate both global and local structure information via a unified L1-norm optimization framework. Unlike generic L2-norm-based LDA, the proposed algorithm explicitly incorporates the local structure information of sample points and is robust to outliers. It overcomes the problem of the singularity of within-class scatter matrix as well. Experiments on several popular datasets demonstrate the effectiveness of the proposed algorithm.
Vorheriger Artikel Statistical moments calculated via integral images in application to landmine detection from Ground Penetrating Radar 3D scans
Nächster Artikel Scalability of correlation clustering

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Belhumeur PN, Hepanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRef
2.
Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233CrossRef
3.
Ji S-W, Ye J-P (2008) Generalized linear discriminant analysis: a unified frame-work and efficient model selection. IEEE Trans Neural Netw 19(10):1768–1782CrossRef
4.
Lu J-W, Plataniotis K, Venetsanopoulos A (2005) Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition. Pattern Recogn Lett 26:181–191CrossRef
5.
Ye J, Li Q (2005) A two-stage linear discriminant analysis via QR decomposition. IEEE Trans Pattern Anal Mach Intell 27(6):929–941CrossRef
6.
Howland P, Park H (2004) Generalizing discriminant analysis using the generalized singular value decomposition. IEEE Trans Pattern Anal Mach Intell 26(8):995–1006CrossRef
7.
Ye J-P (2006) Computational and theoretical analysis of null space and orthogonal linear discriminant analysis. J Mach Learn Res 7:1183–1204MathSciNetMATH
8.
Chen L-F, Mark Liao HY, Ko MT, Lin J-C, Yu G-J (2000) A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recogn 33:1713–1726CrossRef
9.
Yu H, Yang J (2001) A direct LDA algorithm for high-dimensional data-with application to face recognition. Pattern Recogn 34:2067–2070MATHCrossRef
10.
Cai D, He X, Han J (2008) SRDA: an efficient algorithm for large-scale discriminant analysis. IEEE Trans Knowl Data Eng 20(4):1–12CrossRef
11.
Yang J, Yang J-Y (2003) Why can LDA be performed in PCA transformed space? Pattern Recogn 36:563–566CrossRef
12.
Hou C, Zhang C, Wu Y, Jiao Y (2009) Stable local dimensionality reduction approaches. Pattern Recogn 42(9):2054–2066MATHCrossRef
13.
Gao Q, Xu H, Li Y, Xie D (2010) Two-dimensional supervised local similarity and diversity projection. Pattern Recogn 43(10):3359–3363MATHCrossRef
14.
Gao Q, Liu J, Zhang H, Hou J, Yang X (2012) Enhanced fisher discriminant criterion for image recognition. Pattern Recogn 45(10):3717–3724CrossRef
15.
Zhang D, He J, Zhao Y, Luo Z, Minghui D (2014) Global plus local: a complete framework for feature extraction and recognition. Pattern Recogn 47(3):1433–1442MATHCrossRef
16.
Black MJ, Rangarajan A (1996) On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. Int J Comput Vis 19(1):57–91CrossRef
17.
Pang Y, Li X, Yuan Y (2010) Robust tensor analysis with L1-norm. IEEE Trans Circuits Syst Video Technol 20(2):172–178CrossRef
18.
Wang H, Tang Q, Zheng W (2012) L1-norm-based common spatial patterns. IEEE Trans Biomed Eng 59(3):653–662CrossRef
19.
Ke Q, Kanade T (2003) Robust subspace computation using L1 norm. Carnegie Mellon University, Pittsburgh, Technical Report, CMU-CS-03-172
20.
Ke Q, Kanade T (2005) Robust L1 norm factorization in the presence of outliers and missing data by alternative convex programming. In: Proceedings of the IEEE international conferences computer vision pattern recognition, pp 739–746
21.
Ding C, Zhou D, He X, Zha H (2006) R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization. In: Proceedings international conference on machine learning, pp 281–288
22.
Kwak N (2008) Principal component analysis based on L1-norm maximization. IEEE Trans Pattern Anal Mach Intell 30(9):672–1680CrossRef
23.
Li XL, Pang YW, Yuan Y (2009) L1-norm-based 2DPCA. IEEE Trans Syst Man Cybern B Cybern 40(4):1170–1175
24.
Li X, Hua W, Wang H, Zhang Z (2010) Linear discriminant analysis using rotational invariant L1 norm. Neurocomputing 73(13–15):2571–2579CrossRef
25.
Zhou F, Zhang J (2013) Linear discriminant analysis based on L1-norm maximization. IEEE Trans Image Process 22(8):3018–3027MathSciNetMATHCrossRef
26.
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRef
27.
Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRef
28.
Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inf Process Syst 1:585–592
29.
Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) “Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51CrossRef
30.
Cai H-P, Mikolajczyk K, Matas J (2011) Learning linear discriminant projections for dimensionality reduction of image descriptors. IEEE Trans Pattern Anal Mach Intell 33(2):338–352CrossRef
31.
Cui Y, Fan L (2012) A novel supervised dimensionality reduction algorithm: graph-based Fisher analysis. Pattern Recogn 45(4):1471–1481MATHCrossRef
32.
He X, Niyogi P (2004) Locality preserving projections. Adv Neural Inf Process Syst 16:153–160
33.
Shu X, Gao Y, Lu H (2012) Efficient linear discriminant analysis with locality preserving for face recognition. Pattern Recogn 45(5):1892–1898MATHCrossRef
34.
Masashi S (2007) Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J Mach Learn Res 8:1027–1061MATH
35.
Wang N, Tao D, Gao X, Liu W, Li J (2013) Transductive face sketch-photo synthesis. IEEE Trans Neural Netw Learn Syst 24(9):1364–1376CrossRef
36.
Yang J, Zhang D, Yang J-Y, Niu B (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):650–664CrossRef
37.
Loog M, Duin RPW (2004) Heteroscedastic extension of LDA: the Chernoff criterion. IEEE Trans Pattern Anal Mach Intell 26(6):732–739CrossRef
38.
Li X, Hao Z, Huang H (2010) An evolution algorithm with sorted race mechanism for global optimization. In: Proceedings of international conference on machine learning and computing (ICMLC 2010), vol 3, pp 1550–1555
39.
40.
Yang J, Frangi AF, Yang J, Zhang D, Jin Z (2005) KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2):230–244CrossRef
41.
Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans Neural Netw 14(1):117–126CrossRef
42.
43.
44.
Tsai JT, Liu TK, Chou JH (2004) Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Trans Evol Comput 8(4):365–377CrossRef
45.
Leung YW, Wang YP (2001) An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans Evol Comput 5(1):41–53CrossRef
46.
Jenatton R, Obozinski G, Bach F (2010) Structured sparse principal component analysis. In: Proceedings of the international conference artificial intelligence statistics, pp 1–8
47.
Zhong F, Zhang J, Li D (2014) Discriminant locality preserving projections based on L1-norm maximization. IEEE Trans Neural Netw Learn Syst 25(11):2065–2074CrossRef
Metadaten
Titel
A new linear discriminant analysis algorithm based on L1-norm maximization and locality preserving projection
verfasst von
Di Zhang
Xueqiang Li
Jiazhong He
Minghui Du
Publikationsdatum
07.02.2017
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 3/2018
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-017-0594-y

Weitere Artikel der Ausgabe 3/2018

Pattern Analysis and Applications 3/2018 Zur Ausgabe

Industrial and Commercial Application

Unsupervised detection of ruptures in spatial relationships in video sequences based on log-likelihood ratio

Theoretical Advances

A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection

Theoretical Advances

Statistical moments calculated via integral images in application to landmine detection from Ground Penetrating Radar 3D scans

Short Paper

A semi-supervised domain adaptation assembling approach for image classification

Theoretical Advances

Matrix-based subspace analysis with the general norm for image feature extraction

Original Article

BMOG: boosted Gaussian Mixture Model with controlled complexity for background subtraction