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A Short Review of Recent ELM Applications

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Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation (ICC3 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 844))

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Abstract

Extreme Learning Machine has enjoyed an explosive growth in the last years. Starting from a “simple” idea of replacing the slow backpropagation training of the input to hidden units weights by a random sampling, they have been enriched and hybridized in many ways, becoming an accepted tool in the machine learning engineer toolbox. Hence their multiple applications, which are growing in the last few years. The aim of this short review is to gather some glimpses on the state of development and application of ELM. The point of view of the review is factual: ELM are happening, in the sense that many researchers are using them and finding them useful in a wide diversity of applications.

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References

  1. Aghbashlo, M., Shamshirband, S., Tabatabaei, M., Yee, P.L., Larimi, Y.N.: The use of ELM-WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a DI diesel engine running on diesel/biodiesel blends containing polymer waste. Energy 94, 443–456 (2016)

    Article  Google Scholar 

  2. Akusok, A., Miche, Y., Karhunen, J., Bjork, K.-M., Nian, R., Lendasse, A.: Arbitrary category classification of websites based on image content. IEEE Comput. Intell. Mag. 10(2), 30–41 (2015)

    Article  Google Scholar 

  3. Ayerdi, B., Romay, M.G.: Hyperspectral image analysis by spectral-spatial processing and anticipative hybrid extreme rotation forest classification. IEEE Trans. Geosci. Remote Sens. PP(99), 1–13 (2015)

    Google Scholar 

  4. Ayerdi, B., Graña, M.: Hybrid extreme rotation forest. Neural Netw. 52, 33–42 (2014)

    Article  MATH  Google Scholar 

  5. Ayerdi, B., Graña, M.: Hyperspectral image nonlinear unmixing and reconstruction by ELM regression ensemble. Neurocomputing 174(Part A), 299–309 (2016)

    Article  Google Scholar 

  6. Ayerdi, B., Marqués, I., Graña, M.: Spatially regularized semisupervised ensembles of extreme learning machines for hyperspectral image segmentation. Neurocomputing 149(Part A), 373–386 (2015). Advances in Extreme Learning Machines

    Article  Google Scholar 

  7. Azad, N.L., Mozaffari, A., Hedrick, J.K.: A hybrid switching predictive controller based on bi-level kernel-based ELM and online trajectory builder for automotive coldstart emissions reduction. Neurocomputing 173(Part 3), 1124–1141 (2016)

    Article  Google Scholar 

  8. Barreto, G.A., Barros, A.L.B.P.: A robust extreme learning machine for pattern classification with outliers. Neurocomputing 176, 3–13 (2016). Recent Advancements in Hybrid Artificial Intelligence Systems and Its Application to Real-World Problems Selected Papers from the HAIS 2013 Conference

    Article  Google Scholar 

  9. Bazi, Y., Alajlan, N., Melgani, F., AlHichri, H., Malek, S., Yager, R.R.: Differential evolution extreme learning machine for the classification of hyperspectral images. Geosci. Remote Sens. Lett. IEEE 11(6), 1066–1070 (2014)

    Article  Google Scholar 

  10. Bencherif, M.A., Bazi, Y., Guessoum, A., Alajlan, N., Melgani, F., AlHichri, H.: Fusion of extreme learning machine and graph-based optimization methods for active classification of remote sensing images. Geosci. Remote Sens. Lett. IEEE 12(3), 527–531 (2015)

    Article  Google Scholar 

  11. Beyhan, S., Kavaklioglu, K.: Comprehensive modeling of U-tube steam generators using extreme learning machines. IEEE Trans. Nucl. Sci. 62(5), 2245–2254 (2015)

    Article  Google Scholar 

  12. Bisio, F., Decherchi, S., Gastaldo, P., Zunino, R.: Inductive bias for semi-supervised extreme learning machine. Neurocomputing 174(Part A), 154–167 (2016)

    Article  Google Scholar 

  13. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  14. Cao, J., Lin, Z., Huang, G.-B., Liu, N.: Voting based extreme learning machine. Inf. Sci. 185(1), 66–77 (2012)

    Article  MathSciNet  Google Scholar 

  15. Cao, K., Wang, G., Han, D., Bai, M., Li, S.: An algorithm for classification over uncertain data based on extreme learning machine. Neurocomputing 174(Part A), 194–202 (2016)

    Article  Google Scholar 

  16. Chang, N.-B., Bai, K., Chen, C.-F.: Smart information reconstruction via time-space-spectrum continuum for cloud removal in satellite images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 1898–1912 (2015)

    Article  Google Scholar 

  17. Chen, Y., Yao, E., Basu, A.: A 128-channel extreme learning machine-based neural decoder for brain machine interfaces. IEEE Trans. Biomed. Circ. Syst. PP(99), 1 (2015)

    Google Scholar 

  18. Chyzhyk, D., Savio, A., Graña, M.: Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Netw. 68, 23–33 (2015)

    Article  Google Scholar 

  19. Damodaran, B.B., Nidamanuri, R.R., Tarabalka, Y.: Dynamic ensemble selection approach for hyperspectral image classification with joint spectral and spatial information. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2405–2417 (2015)

    Article  Google Scholar 

  20. Deng, W.-Y., Ong, Y.-S., Tan, P.S., Zheng, Q.-H.: Online sequential reduced kernel extreme learning machine. Neurocomputing 174(Part A), 72–84 (2016)

    Article  Google Scholar 

  21. Deng, W.-Y., Ong, Y.-S., Zheng, Q.-H.: A fast reduced kernel extreme learning machine. Neural Netw. 76, 29–38 (2016)

    Article  Google Scholar 

  22. Feng, G., Lan, Y., Zhang, X., Qian, Z.: Dynamic adjustment of hidden node parameters for extreme learning machine. IEEE Trans. Cybern. 45(2), 279–288 (2015)

    Article  Google Scholar 

  23. Fossaceca, J.M., Mazzuchi, T.A., Sarkani, S.: MARK-ELM: application of a novel multiple kernel learning framework for improving the robustness of network intrusion detection. Expert Syst. Appl. 42(8), 4062–4080 (2015)

    Article  Google Scholar 

  24. Frenay, B., Verleysen, M.: Reinforced extreme learning machines for fast robust regression in the presence of outliers. IEEE Trans. Cybern. PP(99), 1–13 (2015)

    Google Scholar 

  25. Gao, X., Chen, Z., Tang, S., Zhang, Y., Li, J.: Adaptive weighted imbalance learning with application to abnormal activity recognition. Neurocomputing 173(Part 3), 1927–1935 (2016)

    Article  Google Scholar 

  26. Grigorievskiy, A., Miche, Y., Käpylä, M., Lendasse, A.: Singular value decomposition update and its application to (inc)-OP-ELM. Neurocomputing 174(Part A), 99–108 (2016)

    Article  Google Scholar 

  27. He, Y.L., Geng, Z.Q., Zhu, Q.X.: Soft sensor development for the key variables of complex chemical processes using a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square. Chemom. Intell. Lab. Syst. 151, 78–88 (2016)

    Article  Google Scholar 

  28. Huang, G., Cambria, E., Toh, K., Widrow, B., Xu, Z.: New trends of learning in computational intelligence [guest editorial]. IEEE Comput. Intell. Mag. 10(2), 16–17 (2015)

    Article  Google Scholar 

  29. Huang, G., Huang, G.-B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)

    Article  MATH  Google Scholar 

  30. Huang, G.-B., Bai, Z., Kasun, L.L.C., Vong, C.M.: Local receptive fields based extreme learning machine. IEEE Comput. Intell. Mag. 10(2), 18–29 (2015)

    Article  Google Scholar 

  31. Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006). Neural Networks Selected Papers from the 7th Brazilian Symposium on Neural Networks (SBRN 2004) 7th Brazilian Symposium on Neural Networks

    Article  Google Scholar 

  32. Huang, S., Wang, B., Qiu, J., Yao, J., Wang, G., Yu, G.: Parallel ensemble of online sequential extreme learning machine based on mapreduce. Neurocomputing 174(Part A), 352–367 (2016)

    Article  Google Scholar 

  33. Huang, X., Yu, H., Liu, X., Jiang, Y., Yan, M.: A single-frame superresolution algorithm for lab-on-a-chip lensless microfluidic imaging. IEEE Des. Test 32(6), 32–40 (2015)

    Article  Google Scholar 

  34. Iosifidis, A., Tefas, A., Pitas, I.: Graph embedded extreme learning machine. IEEE Trans. Cybern. 46(1), 311–324 (2016)

    Article  Google Scholar 

  35. Janakiraman, V.M., Nguyen, X., Assanis, D.: An ELM based predictive control method for HCCI engines. Eng. Appl. Artif. Intell. 48, 106–118 (2016)

    Article  Google Scholar 

  36. Janakiraman, V.M., Nguyen, X., Sterniak, J., Assanis, D.: Identification of the dynamic operating envelope of HCCI engines using class imbalance learning. IEEE Trans. Neural Netw. Learn. Syst. 26(1), 98–112 (2015)

    Article  MathSciNet  Google Scholar 

  37. Javed, K., Gouriveau, R., Zerhouni, N.: SW-ELM: a summation wavelet extreme learning machine algorithm with a priori parameter initialization. Neurocomputing 123, 299–307 (2014). Contains Special Issue Articles: Advances in Pattern Recognition Applications and Methods

    Article  Google Scholar 

  38. Jia, X., Wang, R., Liu, J., Powers, D.M.W.: A semi-supervised online sequential extreme learning machine method. Neurocomputing 174(Part A), 168–178 (2016)

    Article  Google Scholar 

  39. Kiaee, E., Sheikhzadeh, H., Mahabadi, S.E.: Sparse Bayesian mixed-effects extreme learning machine, an approach for unobserved clustered heterogeneity. Neurocomputing 175(Part A), 411–420 (2016)

    Article  Google Scholar 

  40. Lacruz, B., Lahoz, D., Mateo, P.M.: \(\mu \)G2-ELM: an upgraded implementation of \(\mu \)G-ELM. Neurocomputing 171, 1302–1312 (2016)

    Google Scholar 

  41. Li, J., Wang, B., Wang, G., Zhang, Y.: Probabilistic threshold query optimization based on threshold classification using ELM for uncertain data. Neurocomputing 174(Part A), 211–219 (2016)

    Article  Google Scholar 

  42. Li, J., Hua, C., Tang, Y., Guan, X.: A fast training algorithm for extreme learning machine based on matrix decomposition. Neurocomputing 173(Part 3), 1951–1958 (2016)

    Article  Google Scholar 

  43. Li, S., You, Z.-H., Guo, H., Luo, X., Zhao, Z.-Q.: Inverse-free extreme learning machine with optimal information updating. IEEE Trans. Cybern. PP(99), 1 (2015)

    Article  Google Scholar 

  44. Li, W., Chen, C., Hongjun, S., Qian, D.: Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans. Geosci. Remote Sens. 53(7), 3681–3693 (2015)

    Article  Google Scholar 

  45. Lopez-Guede, J.M., Fernandez-Gauna, B., Ramos-Hernanz, J.A.: A L-MCRS dynamics approximation by ELM for reinforcement learning. Neurocomputing 150(Part A), 116–123 (2015)

    Article  Google Scholar 

  46. Xinjiang, L., Liu, C., Huang, M.: Online probabilistic extreme learning machine for distribution modeling of complex batch forging processes. IEEE Trans. Ind. Inf. 11(6), 1277–1286 (2015)

    Article  Google Scholar 

  47. Lu, X., Zhou, C., Huang, M., Lv, W.: Regularized online sequential extreme learning machine with adaptive regulation factor for time-varying nonlinear system. Neurocomputing 174(Part B), 617–626 (2016)

    Article  Google Scholar 

  48. Luo, X., Chang, X., Ban, X.: Regression and classification using extreme learning machine based on L1-norm and L2-norm. Neurocomputing 174(Part A), 179–186 (2016)

    Article  Google Scholar 

  49. Marques, I., Graña, M.: Face recognition with lattice independent component analysis and extreme learning machines. Soft. Comput. 16(9), 1525–1537 (2012)

    Article  Google Scholar 

  50. Mohammed, A.A., Minhas, R., Wu, Q.M.J., Sid-Ahmed, M.A.: Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recogn. 44(10–11), 2588–2597 (2011). Semi-Supervised Learning for Visual Content Analysis and Understanding

    Article  MATH  Google Scholar 

  51. Moreno, R., Corona, F., Lendasse, A., Graña, M., Galvão, L.S.: Extreme learning machines for soybean classification in remote sensing hyperspectral images. Neurocomputing 128, 207–216 (2014)

    Article  Google Scholar 

  52. Othman, E., Bazi, Y., Alajlan, N., AlHichri, H., Melgani, F.: Three-layer convex network for domain adaptation in multitemporal VHR images. IEEE Geosci. Remote Sens. Lett. PP(99), 1–5 (2016)

    Article  Google Scholar 

  53. Peng, Y., Lu, B.-L.: Discriminative manifold extreme learning machine and applications to image and EEG signal classification. Neurocomputing 174(Part A), 265–277 (2016)

    Article  Google Scholar 

  54. Peng, Y., Wang, S., Long, X., Lu, B.-L.: Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing 149(Part A), 340–353 (2015). Advances in Extreme Learning Machines

    Article  Google Scholar 

  55. Peng, Y., Zheng, W.-L., Lu, B.-L.: An unsupervised discriminative extreme learning machine and its applications to data clustering. Neurocomputing 174(Part A), 250–264 (2016)

    Article  Google Scholar 

  56. Principe, J.C., Chen, B.: Universal approximation with convex optimization: gimmick or reality? [discussion forum]. IEEE Comput. Intell. Mag. 10(2), 68–77 (2015)

    Article  Google Scholar 

  57. Qu, B.Y., Lang, B.F., Liang, J.J., Qin, A.K., Crisalle, O.D.: Two-hidden-layer extreme learning machine for regression and classification. Neurocomputing 175(Part A), 826–834 (2016)

    Article  Google Scholar 

  58. Samat, A., Du, P., Liu, S., Li, J., Cheng, L.: E\(^{2}\) LMs: ensemble extreme learning machines for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(4), 1060–1069 (2014)

    Google Scholar 

  59. Scardapane, S., Comminiello, D., Scarpiniti, M., Uncini, A.: Online sequential extreme learning machine with kernels. IEEE Trans. Neural Netw. Learn. Syst. 26(9), 2214–2220 (2015)

    Article  MathSciNet  Google Scholar 

  60. Shao, Z., Er, M.J.: An online sequential learning algorithm for regularized extreme learning machine. Neurocomputing 173(Part 3), 778–788 (2016)

    Article  Google Scholar 

  61. Shojaeilangari, S., Yau, W.-Y., Nandakumar, K., Li, J., Teoh, E.K.: Robust representation and recognition of facial emotions using extreme sparse learning. IEEE Trans. Image Process. 24(7), 2140–2152 (2015)

    Article  MathSciNet  Google Scholar 

  62. Singh, R.P., Dabas, N., Chaudhary, V., Nagendra: Online sequential extreme learning machine for watermarking in DWT domain. Neurocomputing 174(Part A), 238–249 (2016)

    Google Scholar 

  63. Soares, S.G., Araújo, R.: An adaptive ensemble of on-line extreme learning machines with variable forgetting factor for dynamic system prediction. Neurocomputing 171, 693–707 (2016)

    Article  Google Scholar 

  64. Song, J.-L., Hu, W., Zhang, R.: Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine. Neurocomputing 175(Part A), 383–391 (2016)

    Article  Google Scholar 

  65. Song, Y., Zhang, J.: Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine. J. Neurosci. Methods 257, 45–54 (2016)

    Article  Google Scholar 

  66. Sovilj, D., Eirola, E., Miche, Y., Björk, K.-M., Nian, R., Akusok, A., Lendasse, A.: Extreme learning machine for missing data using multiple imputations. Neurocomputing 174(Part A), 220–231 (2016)

    Article  Google Scholar 

  67. Stosic, D., Stosic, D., Ludermir, T.: Voting based q-generalized extreme learning machine. Neurocomputing 174(Part B), 1021–1030 (2016)

    Article  Google Scholar 

  68. Sulistiawati, I.B., Priyadi, A., Qudsi, O.A., Soeprijanto, A., Yorino, N.: Critical clearing time prediction within various loads for transient stability assessment by means of the extreme learning machine method. Int. J. Electr. Power Energ. Syst. 77, 345–352 (2016)

    Article  Google Scholar 

  69. Suri, M., Parmar, V.: Exploiting intrinsic variability of filamentary resistive memory for extreme learning machine architectures. IEEE Trans. Nanotechnol. 14(6), 963–968 (2015)

    Article  Google Scholar 

  70. Tan, P., Xia, J., Zhang, C., Fang, Q., Chen, G.: Modeling and reduction of NOX emissions for a 700 mw coal-fired boiler with the advanced machine learning method. Energy 94, 672–679 (2016)

    Article  Google Scholar 

  71. Tang, J., Deng, C., Huang, G.-B., Zhao, B.: Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Trans. Geosci. Remote Sens. 53(3), 1174–1185 (2015)

    Article  Google Scholar 

  72. Termenon, M., Graña, M., Savio, A., Akusok, A., Miche, Y., Björk, K.-M., Lendasse, A.: Brain MRI morphological patterns extraction tool based on extreme learning machine and majority vote classification. Neurocomputing 174(Part A), 344–351 (2016)

    Article  Google Scholar 

  73. Tissera, M.D., McDonnell, M.D.: Deep extreme learning machines: supervised autoencoding architecture for classification. Neurocomputing 174(Part A), 42–49 (2016)

    Article  Google Scholar 

  74. Vinay, A., Shekhar, V.S., Rituparna, J., Aggrawal, T., Murthy, K.N.B., Natarajan, S.: Cloud based big data analytics framework for face recognition in social networks using machine learning. Procedia Comput. Sci. Big Data, Cloud Comput. Challenges 50, 623–630 (2015)

    Google Scholar 

  75. Wang, N., Sun, J.-C., Er, M.J., Liu, Y.-C.: A novel extreme learning control framework of unmanned surface vehicles. IEEE Trans. Cybern. PP(99), 1 (2015)

    Google Scholar 

  76. Wang, N., Er, M.J., Han, M.: Generalized single-hidden layer feedforward networks for regression problems. IEEE Trans. Neural Netw. Learn. Syst. 26(6), 1161–1176 (2015)

    Article  MathSciNet  Google Scholar 

  77. Wang, Q., Wang, W., Nian, R., He, B., Shen, Y., Björk, K.-M., Lendasse, A.: Manifold learning in local tangent space via extreme learning machine. Neurocomputing 174(Part A), 18–30 (2016)

    Google Scholar 

  78. Wang, X., Hongli, H., Liu, X.: Multisensor data fusion techniques with ELM for pulverized-fuel flow concentration measurement in cofired power plant. IEEE Trans. Instrum. Measur. 64(10), 2769–2780 (2015)

    Article  Google Scholar 

  79. Wang, Y., Xie, Z., Xu, K., Dou, Y., Lei, Y.: An efficient and effective convolutional auto-encoder extreme learning machine network for 3d feature learning. Neurocomputing 174(Part B), 988–998 (2016)

    Article  Google Scholar 

  80. Wang, Y., Hao, Y., Ni, L., Huang, G.-B., Yan, M., Weng, C., Yang, W., Zhao, J.: An energy-efficient nonvolatile in-memory computing architecture for extreme learning machine by domain-wall nanowire devices. IEEE Trans. Nanotechnol. 14(6), 998–1012 (2015)

    Article  Google Scholar 

  81. Wong, S.Y., Yap, K.S., Yap, H.J.: A constrained optimization based extreme learning machine for noisy data regression. Neurocomputing 171, 1431–1443 (2016)

    Article  Google Scholar 

  82. Xia, J., Mura, M.D., Chanussot, J., Du, P., He, X.: Random subspace ensembles for hyperspectral image classification with extended morphological attribute profiles. IEEE Trans. Geosci. Remote Sens. 53(9), 4768–4786 (2015)

    Article  Google Scholar 

  83. Yang, Y., Wu, Q.M.J.: Multilayer extreme learning machine with subnetwork nodes for representation learning. IEEE Trans. Cybern. PP(99), 1–14 (2015)

    Google Scholar 

  84. Yang, Y., Wu, Q.M.J., Wang, Y., Zeeshan, K.M., Lin, X., Yuan, X.: Data partition learning with multiple extreme learning machines. IEEE Trans. Cybern. 45(8), 1463–1475 (2015)

    Article  Google Scholar 

  85. Yang, Z.-X., Zhao, G.-S., Rong, H.-J., Yang, J.: Adaptive backstepping control for magnetic bearing system via feedforward networks with random hidden nodes. Neurocomputing 174(Part A), 109–120 (2016)

    Article  Google Scholar 

  86. Zhao, R., Mao, K.: Semi-random projection for dimensionality reduction and extreme learning machine in high-dimensional space. IEEE Comput. Intell. Mag. 10(3), 30–41 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country, Leioa, Spain

    Manuel Graña

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  1. Manuel Graña
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Editors and Affiliations

  1. PSG College of Technology, Coimbatore, India

    Geetha Ganapathi

  2. BITS Pilani, KK Birla, Goa, India

    Arumugam Subramaniam

  3. University of the Basque Country, San Sebastian, Spain

    Manuel Graña

  4. PSG College of Technology, Coimbatore, India

    Suresh Balusamy

  5. PSG College of Technology, Coimbatore, India

    Rajamanickam Natarajan

  6. PSG College of Technology, Coimbatore, India

    Periakaruppan Ramanathan

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Graña, M. (2018). A Short Review of Recent ELM Applications. In: Ganapathi, G., Subramaniam, A., Graña, M., Balusamy, S., Natarajan, R., Ramanathan, P. (eds) Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation. ICC3 2017. Communications in Computer and Information Science, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-13-0716-4_1

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