Consensus-Locally Linear Embedding (C-LLE): Application to Prostate Cancer Detection on Magnetic Resonance Spectroscopy

  • Pallavi Tiwari
  • Mark Rosen
  • Anant Madabhushi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)


Locally Linear Embedding (LLE) is a widely used non-linear dimensionality reduction (NLDR) method that projects multi-dimensional data into a low-dimensional embedding space while attempting to preserve object adjacencies from the original high-dimensional feature space. A limitation of LLE, however, is the presence of free parameters, changing the values of which may dramatically change the low dimensional representations of the data. In this paper, we present a novel Consensus-LLE (C-LLE) scheme which constructs a stable consensus embedding from across multiple low dimensional unstable LLE data representations obtained by varying the parameter (κ) controlling locally linearity. The approach is analogous to Breiman’s Bagging algorithm for generating ensemble classifiers by combining multiple weak predictors into a single predictor. In this paper we demonstrate the utility of C-LLE in creating a low dimensional stable representation of Magnetic Resonance Spectroscopy (MRS) data for identifying prostate cancer. Results of quantitative evaluation demonstrate that our C-LLE scheme has higher cancer detection sensitivity (86.90%) and specificity (85.14%) compared to LLE and other state of the art schemes currently employed for analysis of MRS data.


Magnetic Resonance Spectroscopy Independent Component Analysis Peak Detection Independent Component Analysis Locally Linear Embedding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Roweis, S., Saul, L.: Fit Locally Think Globally. JMLR 4, 119–155 (2003)zbMATHGoogle Scholar
  2. 2.
    Lee, G., Rodriguez, C., Madabhushi, A.: Investigating the Efficacy of Nonlinear Dimensionality Reduction Schemes in Classifying Gene- and Protein-Expression Studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2008)Google Scholar
  3. 3.
    Lin, J., Zha, J.: Riemann Manifold Learning. IEEE Transactions on pattern Analysis 30, 796–809 (2008)CrossRefGoogle Scholar
  4. 4.
    Wang, J., Zhang, Z., Zha, H.: Adaptive Manifold Learning. In: NIPS (2004)Google Scholar
  5. 5.
    Breiman, L.: Bagging Predictors. Machine Learning, 123–140 (1996)Google Scholar
  6. 6.
    Tiwari, P., Rosen, M., Madabhushi, A.: A Hierachical Unsupervised Spectral Clustering Scheme for Detection of Prostate Cancer from Magnetic Resonance Spectroscopy. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 278–286. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Venna, J., et al.: Local Multidimensional Scaling. Neural Networks 19(6) (2006)Google Scholar
  8. 8.
    Zakian, K., et al.: Correlation of Proton MRSI with gleason score based on sept-section Pathologic Analysis after Radical Prostatectomy. Radiology 234, 804–814 (2005)CrossRefGoogle Scholar
  9. 9.
    Kurhanewicz, J., et al.: Prostate Depiction at Endorectal MR Spectroscopic Imaging: Investigation of a Standardized Evaluation System. Radiology 233, 701–708 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Pallavi Tiwari
    • 1
  • Mark Rosen
    • 2
  • Anant Madabhushi
    • 1
  1. 1.Department of Biomedical EngineeringRutgers UniversityUSA
  2. 2.Department of RadiologyUniversity of PennsylvaniaUSA

Personalised recommendations