Skip to main content
SpringerLink
Log in

A survey on feature extraction for pattern recognition

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

In research of pattern recognition, we always want to achieve the correct classification rate according to the characteristics required. Feature extraction greatly affects the design and performance of the classifier, and it is one of the core issue of PR research. As an important component of pattern recognition, feature extraction has been paid close attention by many scholars, and currently has become one of the research hot spots in the field of pattern recognition. This article gives a general discussion of feature extraction, includes linear feature extraction and nonlinear feature extraction, and introduces the frontier methods of this field, at last discusses the development tendency of feature extraction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aizerman M, Branverman E, Rozonoer L (1964) Theoretical foundations of the potential foundation method in patten recognitions. Autom emote control 25: 821–837

    Google Scholar 

  • Bian Z, Zhang (2000) Pattern Recognition. 2. Tsinghua University Press, Beijing, China

    Google Scholar 

  • Cheng Z, Chen D (2002) Integrated strategy of pattern classification and its application. J Zhejiang Univ (Engineering Science) 36(6): 601–606

    Google Scholar 

  • Comon P (1994) Independent component analysis-A new concept. Signal Process 36(3): 287–314

    Article  MATH  Google Scholar 

  • Ding S, Jin F, Wang J (2003) Information feature analysis and selection of orthogonal transformation. Acta Geodaetica Et Cartographic Sinica 32(1): 73–77

    Google Scholar 

  • Ding S, Jin F, Wang J (2004) New PCA feature compression algorithm based on information theory. Mini-micro Syst 25(4): 694–697

    Google Scholar 

  • Ding S, Jin F (2003) Information characteristics K-L transform based on information entropy. Trans Nonferr Met Soc China 13(3): 729–734

    Google Scholar 

  • Ding S, Jin F, Shi Z (2005) Information feature compression based on partial least squares. J Comput Aided Des Comput Graph 17(2): 368–371

    Google Scholar 

  • Ding S, Jin F, Wang X et al (2005) Information feature compression algorithm based on SCEC. Mini-micro Sys 26(7): 1202–1205

    Google Scholar 

  • Fan Y, Chen Y, Sun W et al (2005) Algorithm for bi-directional reduce feature data based on the principal component analysis and immune clustering. Acta Simulata Systematica Sinica 17(1): 148–153

    Google Scholar 

  • Fang K (1989) Practical multivariate statistic analysis. East China Normal University Press, Shanghai

    Google Scholar 

  • Foman G (2003) An exnetsive empirical study of feature selection metrics for text classification. J Mach Learn Res 3: 1289–1305

    Google Scholar 

  • Friedman JH, Tukey JW (1974) A projection pursuit algorithm for exploratory data analysis. IEEE Trans Comput C- 23(9): 881–890

    Article  MATH  Google Scholar 

  • Fujarewicz K, Wiench M (2003) Selecting differentially expressed genes for colon tumor Classification. Int J Appl Math Comput Sci 13(3): 327–335

    MathSciNet  MATH  Google Scholar 

  • Huang R, He M, Yang S (2007) A Margin Based Feature Extraction Algorithm for the Small Sample Size problem. Chin J Comput 30(7): 1173–1178

    Google Scholar 

  • Jensn D, Cohen P (2000) Multipe comparisons in induction algorithms. Mach Learn 38(3): 309–338

    Article  Google Scholar 

  • Jin Z, Yang J, Lu J (1999) An optimal set of uncorrelated discriminant features. Chin J Comput 22(10): 1105–1110

    Google Scholar 

  • Jin Z, Yang J, Hu J et al (2001) Face recognition based on the uncorrelated discriminant transformation. Pattern Recognit 34(7): 1405–1416

    Article  MATH  Google Scholar 

  • Johnson A et al (1998) Applied multivariate satatistical analysis. 4th. Prentice-Hall, Inc

    Google Scholar 

  • Johnson JL (1994) Pulse-coupled neural nets: translation, rotation, scale, distortion and intensity signal invariance for images. Appl Opt 33(26): 6239–6253

    Article  Google Scholar 

  • Jolliffe IT (1986) Principal component analysis. Springer, Berlin

    Google Scholar 

  • Jones MC, Sibson R (1987) What is projection pursuit. J Royal Stat Soc Ser A(General) 150(1): 1–36

    Article  MathSciNet  MATH  Google Scholar 

  • Jutten C, Herault J (1988) Independent component analysis versus PCA. In: Proceeding of European signal processing Conference, pp 287-314

  • Jutten C, Herault J (1991) Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Process 24(1): 1–10

    Article  MATH  Google Scholar 

  • Karhunen J, Hyvarinen A, Vigario R (1997) Applications of neural blind separation to signal and image processing. In: Proceedings of the IEEE 1997 international conference on acoustics, speech, and signal processing, pp 131–134

  • Lewis DD (1992) An evaluation of phrasal and clustered representations on a text categorization task. In: Proceedings of the 15th ACM international conference on research and development in information retrieval, Copenhagen, pp 246–254

  • Li H (1995) Fuzzy Information eentropy criterion for fault features evaluation. Inf Control 24(5): 301–304

    Google Scholar 

  • Li S, Ji X, Zhu S (1999) The application study of entropy analysis method in feature extraction. J North China Inst Tech 20(3): 278–281

    Google Scholar 

  • Liu Z, Sheng S (2004) Research on the method of fault feature extraction. Appl Electron Tech 11(19): 19–21

    Google Scholar 

  • Mao Y, Xia X, Xia Z (2007) A survey for study of feature selection algorithms. Pattern Recognit Artif Intell 20(2): 211–218

    Google Scholar 

  • Mcculloch S, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bulletin Math Biol 5(4): 115–133

    MathSciNet  MATH  Google Scholar 

  • Mercer J (1909) Functions of postitive and negative type and their connection with the theory of integral equations. Philos Trans R Soc Lond 209: 415–446

    Article  MATH  Google Scholar 

  • Mika S, Ratsch G, Weston J et al (1999) Fisher discriminant analysis with kernels. In: Neural networks for signal processing IX. IEEE Press, New York, 41–48

  • Narendra PM, Fukunaga K (1977) A branch and bound algorithm for feature subset selection. IEEE Trans Comput 26(9): 917–922

    Article  MATH  Google Scholar 

  • Nguyen HS, Nguyen SH, Skowron A (1996) Searching for features defined by hyperplanes. Found Intell Sys 1079: 366–375

    Google Scholar 

  • Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290: 2323–2326

    Article  Google Scholar 

  • Scholkopf B, Smola A, Mulle KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5): 1299–1319

    Article  Google Scholar 

  • Setiono R (2000) Generating concise and accurate classification rules for breast cancer diagnosis. Artif Intell Med 18(3): 205–219

    Article  Google Scholar 

  • Seung Sebastian H, Lee D (2000) The manifold ways of perception. Science 290(12): 2268–2269

    Article  Google Scholar 

  • Shan S, Chen Y, Cheng Y (2003) Data mining-concept, models,methods and algorithms. Tsinghua University Press, Beijing

    Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. BSTJ 5(1): 379–423

    Google Scholar 

  • Shawe-Taylor J, Williams C (2003) The stability of kernel principal components analysis and its relation to the process eigenspectrum. In: Advances in neural information processing systems 15. MIT Press, USA

  • Shen D (1999) Discriminative wavelet shape descriptors for recognition of 2-D patterns. Pattern Recognit 32(2): 151–165

    Article  Google Scholar 

  • Shi Z (2002) Knowledge discovery. Tsinghua University Press, Beijing

    Google Scholar 

  • Simek K, Fujarewicz K, Swierniak A et al (2004) Using SVD and SVM methods for selection, classification, clustering and modeling of DNA microarray data. Eng Appl Artif Intel 17(4): 417–427

    Article  Google Scholar 

  • Smon H (2001) Neural networks: a comprehensive foundation, 2nd edn. Tsinghua University Press, Beijing

    Google Scholar 

  • Song F, Gao X, Liu S (2005) Dimensionality reduction in statistical pattern recognition and low loss dimensionality reduction. Chin J Comput 28(11): 1915–1922

    Google Scholar 

  • Sun J (2002) Modern pattern recognition. Defense University of Science and Technology Publishing House, Changsha

    Google Scholar 

  • Sun P, Xu Z, Shen J (2004a) Nonlinear canonical correlation analysis for discrimination based on kernel methods. Chin J Comput 27(6): 789–795

    Google Scholar 

  • Sun Z, Bebis G, Miller R (2004b) Object detection using feature subset selection. Pattern Recognit 37(11): 2165–2176

    Article  Google Scholar 

  • Tenenbaum J, Silva D, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500): 2319–2323

    Article  Google Scholar 

  • Vafaie H, De Jong K (1993) Robust feature selection algorithms. In: Proceedings of IEEE conference on tools with artificial intelligence, pp 356-363

  • Wang H, Zheng J, Yao Z (2006) Application of dimension reduction on using improved LLE based on clustering. J Comput Res Dev 43(8): 1485–1490

    Article  Google Scholar 

  • Wang Z, Wang H, Leng L (2005) Face recognition combining the null space approach and the fractional LDA. Comput Appl 25(11): 2586–2588

    Google Scholar 

  • Wiener E, Pedersen JO, Weigend AS (1995) A neural network approach to topic spotting. In: Proceedings of the 4th annual symposium on document analysis and information retrieval, pp 317-332

  • Yang J, Frangi AF, Yan J et al (2005) KPCA plus IDA: A complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2): 230–244

    Article  Google Scholar 

  • Yang Z, Li Y, Hu D (2002) Independent component analysis: a survey. J Autom 28(5): 762–772

    MathSciNet  Google Scholar 

  • Yuen P, Lai J (2001) Face representation using independent component analysis. Pattern Recognit 34(3): 545–553

    Google Scholar 

  • Zeng H, Yuan Z (1999) About a new approach of selection and reduction on system feature in pattern recognition. J Sichuan Inst Light Ind Chem Technol 12(4): 1–5

    Google Scholar 

  • Zhang H, Sun G (1999) Tabu search algorithm for feature selection. J Autom 25(4): 457–466

    Google Scholar 

  • Zhang X (1998) Dynamic programming method for feature selection. J Autom 24(5): 675–668

    Google Scholar 

  • Zhang Y, Fang K (1997) Practical multivariate statistic analysis. Science Press, Beijing

    Google Scholar 

  • Zhou D, Gao W, Zhao D (2003) Face recognition based on singular value decomposition and discriminant KL projection. J Softw 14(4): 783–789

    MATH  Google Scholar 

  • Zhu D, Wu C, Qin W (1999) Multivariate statistic analysis and software SAS. Southeast University Press, Nanjing

    Google Scholar 

  • Zhu Q, Wu B, Wan N (2006) An interest point detect method to stereo images with good repeatability and information content. Acta Electron Sinica 34(2): 205–209

    Google Scholar 

  • Zhu X (2000) Extracting geological structure information by multi-principal component analysis. J Remote Sens 4(4): 299–303

    Google Scholar 

  • Zhuang Z, Zhang A, Li F (2007) Based on an optimized LDA algorithm for face recognition. J Electron Inf Technol 29(9): 2047–2049

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, 221116, Xuzhou, China

    Shifei Ding, Hong Zhu, Weikuan Jia & Chunyang Su

Authors
  1. Shifei Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weikuan Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chunyang Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shifei Ding.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ding, S., Zhu, H., Jia, W. et al. A survey on feature extraction for pattern recognition. Artif Intell Rev 37, 169–180 (2012). https://doi.org/10.1007/s10462-011-9225-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-011-9225-y

Keywords

Search

Navigation