Extended hierarchical extreme learning machine with multilayer perceptron

For learning in big datasets, the classification performance of ELM might be low due to input samples are not extracted features properly. To address this problem, the hierarchical extreme learning machine (H-ELM) framework was proposed based on the hierarchical learning architecture of multilayer perceptron. H-ELM composes of two parts; the first is the unsupervised multilayer encoding part and the second part is the supervised feature classification part. H-ELM can give higher accuracy rate than of the traditional ELM. However, it still has to enhance its classification performance. Therefore, this paper proposes a new method namely as the extending hierarchical extreme learning machine (EH-ELM). For the extended supervisor part of EH-ELM, we have got an idea from the two-layers extreme learning machine. To evaluate the performance of EH-ELM, three different image datasets; Semeion, MNIST, and NORB, were studied. The experimental results show that EH-ELM achieves better performance than of H-ELM and the other multi-layer framework.

[1]  L. C. Kasun,et al.  Representational Learning with Extreme Learning Machine for Big Data Liyanaarachchi , 2022 .

[2]  Jing J. Liang,et al.  Two-hidden-layer extreme learning machine for regression and classification , 2016, Neurocomputing.

[3]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[4]  Li Chao-feng,et al.  Short-term power load forecasting method based on improved extreme learning machine , 2012 .

[5]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[6]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[7]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[8]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[9]  Y. LeCun,et al.  Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[10]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[11]  Guang-Bin Huang,et al.  Extreme Learning Machine for Multilayer Perceptron , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[13]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[14]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[15]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[16]  Shin'ichi Tamura,et al.  Capabilities of a four-layered feedforward neural network: four layers versus three , 1997, IEEE Trans. Neural Networks.

[17]  Junfei Qiao,et al.  Hierarchical extreme learning machine for feedforward neural network , 2014, Neurocomputing.

[18]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[19]  Q. M. Jonathan Wu,et al.  Human action recognition using extreme learning machine based on visual vocabularies , 2010, Neurocomputing.

[20]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[22]  Dipankar Das,et al.  Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining , 2013, IEEE Intelligent Systems.

[23]  Yong Yang,et al.  Leukocyte image segmentation by visual attention and extreme learning machine , 2011, Neural Computing and Applications.

[24]  Guang-Bin Huang,et al.  Learning capability and storage capacity of two-hidden-layer feedforward networks , 2003, IEEE Trans. Neural Networks.

[25]  Q. M. Jonathan Wu,et al.  Human face recognition based on multidimensional PCA and extreme learning machine , 2011, Pattern Recognit..

[26]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[27]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.