Clustering in extreme learning machine feature space

He, Qing; Jin, Xin; Du, Changying; Zhuang, Fuzhen; Shi, Zhongzhi

doi:10.1016/j.neucom.2012.12.063

Cited by 94 publications

(47 citation statements)

References 27 publications

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“…In this section, we compare our method (the k-means clustering algorithm in optimal feature space) with the classical clustering methods which include k-means clustering algorithm on original data sets, kernel clustering method [13], spectral clustering method [14], and ELM kmeans clustering method [15].…”

Section: Resultsmentioning

confidence: 99%

“…Clustering data in a feature space has been previously proposed in some earlier published papers such as [15], where the standard k-means method was used in ELM feature space. Using graph Laplacian and ELM mapping technique, we develop an optimal weight matrix W for feature mapping.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, another clustering method uses extreme learning machine (ELM) feature mapping technique [15], which can map the original data to a higher-dimensional ELM feature space, and then use the traditional clustering method on the feature vectors.…”

Section: Introductionmentioning

confidence: 99%

“…Comparing with the original data, the produced feature vectors can be clustered more accurately and easily. For this reason, the ELM feature vectors are used for clustering instead of input vectors in [15].…”

Section: Introductionmentioning

confidence: 99%

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An optimal method for data clustering

Xie

Mei

et al. 2015

Neural Comput & Applic

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An algorithm for optimizing data clustering in feature space is studied in this work. Using graph Laplacian and extreme learning machine (ELM) mapping technique, we develop an optimal weight matrix W for feature mapping. This work explicitly performs a mapping of the original data for clustering into an optimal feature space, which can further increase the separability of original data in the feature space, and the patterns points in same cluster are still closely clustered. Our method, which can be easily implemented, gets better clustering results than some popular clustering algorithms, like k-means on the original data, kernel clustering method, spectral clustering method, and ELM k-means on data include three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, and Wine database).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An optimal method for data clustering

Xie

Mei

et al. 2015

Neural Comput & Applic

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“…Recently, ELM has been applied to semi-supervised learning and clustering, such as ELM k-means algorithm [9], and unsupervised extreme learning machine (US-ELM) k-means algorithm [10], semi-supervised extreme learning machine (SS-ELM).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data

Zhang

Ding

2016

Memetic Comp.

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Extreme learning machine (ELM) not only is an effective classifier in supervised learning, but also can be applied on unsupervised learning and semi-supervised learning. The model structure of unsupervised extreme learning machine (US-ELM) and semi-supervised extreme learning machine (SS-ELM) are same as ELM, the difference between them is the cost function. We introduce kernel function to US-ELM and propose unsupervised extreme learning machine with kernel (US-KELM). And SS-KELM has been proposed. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and Wavelet kernel functions have been widely used in support vector machine. Therefore, to realize a combination of the wavelet kernel function, US-ELM, and SS-ELM, unsupervised extreme learning machine with wavelet kernel function (US-WKELM) and semi-supervised extreme learning machine with wavelet kernel function (SS-WKELM) are proposed in this paper. The experimental results show the feasibility and validity of US-WKELM and SS-WKELM in clustering and classification.

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