Dimension Reduction With Extreme Learning Machine

Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP),...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 25; H. 8; S. 3906 - 3918
Hauptverfasser:	Kasun, Liyanaarachchi Lekamalage Chamara, Yang, Yan, Huang, Guang-Bin, Zhang, Zhengyou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.08.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms auto-encoder (AE) Dimension reduction Extreme Learning Machine (ELM) Learning Machine learning Machine learning algorithms Mathematical model Neural networks Noise Non-negative Matrix Factorization (NMF) Nonlinearity Object recognition Principal component analysis Principal Component Analysis (PCA) Principal components analysis random projection (RP) Reduction Regression analysis Subspaces Support vector machines Extreme learning machine (ELM) auto-encoder (AE) dimension reduction principal component analysis (PCA) random projection (RP) non-negative matrix factorization (NMF)
ISSN:	1057-7149, 1941-0042, 1941-0042
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Abstract	Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.
AbstractList	Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error. Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.
Author	Yang, Yan Kasun, Liyanaarachchi Lekamalage Chamara Huang, Guang-Bin Zhang, Zhengyou
Author_xml	– sequence: 1 givenname: Liyanaarachchi Lekamalage Chamara orcidid: 0000-0002-4078-3877 surname: Kasun fullname: Kasun, Liyanaarachchi Lekamalage Chamara email: chamarak001@ntu.edu.sg organization: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore – sequence: 2 givenname: Yan surname: Yang fullname: Yang, Yan email: y.yang@nwpu.edu.cn organization: Energy Research Institute, Nanyang Technological University, Singapore – sequence: 3 givenname: Guang-Bin surname: Huang fullname: Huang, Guang-Bin email: egbhuangg@ntu.edu.sg organization: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore – sequence: 4 givenname: Zhengyou surname: Zhang fullname: Zhang, Zhengyou email: zhang@microsoft.com organization: Microsoft Corporation, Redmond, WA, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27214902$$D View this record in MEDLINE/PubMed
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Snippet	Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of...
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SubjectTerms	Algorithms auto-encoder (AE) Dimension reduction Extreme Learning Machine (ELM) Learning Machine learning Machine learning algorithms Mathematical model Neural networks Noise Non-negative Matrix Factorization (NMF) Nonlinearity Object recognition Principal component analysis Principal Component Analysis (PCA) Principal components analysis random projection (RP) Reduction Regression analysis Subspaces Support vector machines
Title	Dimension Reduction With Extreme Learning Machine
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