Discriminative locally linear mapping for medical diagnosis

  • Ping He
  • Xincheng Chang
  • Xiaohua XuEmail author
  • Zhijun Zhang
  • Tianyu Jing
  • Yuan Lou


Medical diagnosis based on machine learning has received growing interest in recent years. However, traditional classification algorithms often fail to appropriately deal with medical datasets because of their high dimensionality. Manifold learning is a branch of nonlinear dimension reduction algorithms that can map the high dimensional data into a low-dimensional space. In this paper, we propose a novel manifold-based medical diagnosis algorithm named Discriminative Locally Linear Mapping (DL2M). DL2M is built on the basis of the well-known manifold leaning algorithm LLE (Locally Linear Embedding). It incorporates the discriminative information of training data into the manifold transformation of LLE, and then propagates the discriminative mapping into out-of-sample extension. DL2M is not only advantageous in preserving the local structure of original manifold, but also maps the different classes of data as far as possible in the low-dimensional feature space. The time complexity of DL2M algorithm is also discussed. Sufficient experimental results demonstrate that our method exhibits promising classification performance on the real-world medical datasets.


Medical diagnosis Manifold learning Classification Locally linear embedding Out-of-sample extension 



Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( As such, the investigator within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: content/uploads/how to apply/ADNI Acknowledgement List.pdf.

This research was supported in part by the Chinese National Natural Science Foundation with Grant nos. 61402395, 61472343, 61702441 and 61802336, Natural Science Foundation of Jiangsu Province under contracts BK20140492 and BK20151314, Jiangsu government scholarship funding, Jiangsu overseas research and training program for university prominent young and middle-aged teachers and presidents.


  1. 1.
    Adankon MM, Cheriet M (2002) Support vector machine. Computer Science 1(4):1–28zbMATHGoogle Scholar
  2. 2.
    Bruijne MD (2016) Machine learning approaches in medical image analysis: from detection to diagnosis. Med Image Anal 33:94–97CrossRefGoogle Scholar
  3. 3.
    Chen X, Li Q, Song Y (2012) Supervised geodesic propagation for semantic label transfer. European Conference on Computer Vision, vol 7574 (1), pp 553–565Google Scholar
  4. 4.
    De Ridder D, Duin R P W (2002) Locally linear embedding for classification. Pattern recognition group. Dept. of Imaging Science & Technology, Delft University of Technology, Delft, The Netherlands, Tech. Rep. PH-2002-01:1-12Google Scholar
  5. 5.
    Jin X, Li Y, Liu N et al (2019) Single reference image based scene relighting via material guided filtering. Opt Laser Technol 110:7–12CrossRefGoogle Scholar
  6. 6.
    Landgrebe D (2002) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cy-s 21(3):660–674MathSciNetGoogle Scholar
  7. 7.
    Li Q, Chen X, Song Y (2014) Geodesic propagation for semantic labeling. IEEE Trans Image Process 23(11):4812–4825MathSciNetCrossRefGoogle Scholar
  8. 8.
    Litjens G, Kooi T, Bejnordi BE (2017) A survey on deep learning in medical image analysis. Med Image Anal 42(9):60–88CrossRefGoogle Scholar
  9. 9.
    Liu F, Zhang W (2016) Local linear Laplacian eigenmaps: a direct extension of LLE. Pattern Recogn Lett 75:30–35CrossRefGoogle Scholar
  10. 10.
    Liu X, Deng S, Yin J (2009) Locality sensitive discriminant analysis based on matrix representation. J Zhejiang Univ-sc A 2:019Google Scholar
  11. 11.
    Liu X, Tosun D, Weiner MW (2013) Locally linear embedding (LLE) for MRI based Alzheimer’s disease classification. Neuroimage 83:148–157CrossRefGoogle Scholar
  12. 12.
    Lu H, Li B, Zhu J et al (2016) Wound intensity correction and segmentation with convolutional neural networks. Concurr Comp-pract E 29(6):1–10Google Scholar
  13. 13.
    Lu H, Li Y, Chen M et al (2017) Brain intelligence: go beyond artificial intelligence. Mobile Netw Appl 23(2):368–375CrossRefGoogle Scholar
  14. 14.
    Ma Y, Zhu L (2013) A review on dimension reduction. Int Stat Rev 81(1):134–150MathSciNetCrossRefGoogle Scholar
  15. 15.
    Nasser M (2007) Pattern recognition and machine learning. J Electron Imaging 16(4):140–155MathSciNetGoogle Scholar
  16. 16.
    Platt J (1998) A fast algorithm for training support vector machines. J Inf Technol 2(5):1–28Google Scholar
  17. 17.
    Ridder DD, Kouropteva O, Okun O (2003) Supervised locally linear embedding. In: Joint international conference on artificial neural networks and neural information processing. Springer-Verlag, pp 333–341Google Scholar
  18. 18.
    Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRefGoogle Scholar
  19. 19.
    Sung F, Yang Y (2018) Learning to compare: relation network for few shot learning. In: 2018 IEEE conference on computer vision and pattern recognition (CVPR)Google Scholar
  20. 20.
    Trambaiolli LR, Lorena AC, Fraga FJ et al (2011) Improving Alzheimer’s disease diagnosis with machine learning techniques. Clin Eeg Neurosci 42(3):160CrossRefGoogle Scholar
  21. 21.
    Wang D, Lu H (2014) Visual tracking via probability continuous outlier model. In: 2014 IEEE conference on computer vision and pattern recognition (CVPR), pp 3478–3485Google Scholar
  22. 22.
    Wang D, Lu H, Xiao Z, Yang M (2015) Inverse sparse tracker with a locally weighted distance metric. IEEE Trans Image Process 24(9):2646MathSciNetCrossRefGoogle Scholar
  23. 23.
    Wang D, Lu H, Yang MH (2016) Robust visual tracking via least soft-threshold squares. IEEE Trans Circ Syst Vid Technol 26(9):1709–1721CrossRefGoogle Scholar
  24. 24.
    Weston J (1999) Support vector machines for multi-class pattern recognition. In: Proc. European symposium on artificial neural networks, vol 17, pp 219–224Google Scholar
  25. 25.
    Xiang S, Nie F, Zhang C (2009) Nonlinear dimensionality reduction with local spline embedding. IEEE Trans Knowl Data Eng 21(9):1285–1298CrossRefGoogle Scholar
  26. 26.
    Xu X, He L, Lu H et al (2018) Deep adversarial metric learning for cross-modal retrieval. World Wide Web.
  27. 27.
    Yan S, Xu D, Zhang B (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51CrossRefGoogle Scholar
  28. 28.
    Yang X, Liu C, Wang Z (2017) Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI. Med Image Anal 42:212–227CrossRefGoogle Scholar
  29. 29.
    Zhang S, Li X, Zong M (2017) Learning k, for kNN classification. ACM Trans Intell Syst Technol 8(3):43Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Ping He
    • 1
  • Xincheng Chang
    • 1
  • Xiaohua Xu
    • 1
    Email author
  • Zhijun Zhang
    • 1
  • Tianyu Jing
    • 1
  • Yuan Lou
    • 1
  1. 1.Department of Computer ScienceYangzhou UniversityYangzhouChina

Personalised recommendations