Stochastic Proximity Embedding: Methods and Applications

Agrafiotis, Dimitris; Xu, Huafeng; Zhu, Fangqiang; Bandyopadhyay, Deepak; Liu, Pu

doi:10.1002/minf.201000134

Cited by 29 publications

(16 citation statements)

References 45 publications

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“…To testify the superiority of our method, five common dimensionality reduction tools combined with sparse filtering are adopted to process the gearbox dataset respectively. The five tools are: principal component analysis (PCA) [25], locality preserving projection (LPP) [26], Sammon mapping (SM) [27], linear discriminant analysis (LDA) [28], and stochastic proximity embedding (SPE) [29]. The classification results by the five methods are shown in Fig.…”

Section: Diagnosis Resultsmentioning

confidence: 99%

An automatic feature extraction method and its application in fault diagnosis

Wang¹,

Li²,

Jiang³

et al. 2017

J. vibroeng.

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The main challenge of fault diagnosis is to extract excellent fault feature, but these methods usually depend on the manpower and prior knowledge. It is desirable to automatically extract useful feature from input data in an unsupervised way. Hence, an automatic feature extraction method is presented in this paper. The proposed method first captures fault feature from the raw vibration signal by sparse filtering. Considering that the learned feature is high-dimensional data which cannot achieve visualization, -distributed stochastic neighbor embedding ( -SNE) is further selected as the dimensionality reduction tool to map the learned feature into a three-dimensional feature vector. Consequently, the effectiveness of the proposed method is verified using gearbox and bearing experimental datas. The classification results show that the hybrid method of sparse filtering and -SNE can well extract discriminative information from the raw vibration signal and can clearly distinguish different fault types. Through comparison analysis, it is also validated that the proposed method is superior to the other methods.

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Section: Diagnosis Resultsmentioning

confidence: 99%

An automatic feature extraction method and its application in fault diagnosis

Wang¹,

Li²,

Jiang³

et al. 2017

J. vibroeng.

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show abstract

“…Here, we present a method for visualizing and interpreting high‐dimensional chemical data, which is complementary to SOM and PCA projection and overcomes some of their disadvantages and limitations. The projection is based on stochastic proximity embedding (SPE) 11. SPE embeds data in a low‐dimensional space in such a way that pairwise distances between compounds are preserved.…”

Section: Results Of Retrospective Virtual Screening Given As Average mentioning

confidence: 99%

From Virtual Screening to Bioactive Compounds by Visualizing and Clustering of Chemical Space

Klenner

Hähnke

Geppert

et al. 2011

Molecular Informatics

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“…The method of SPE [70,71] performs optimization of a random initial configuration of the system to minimize the violation of predefined constraints, for example, distances between the points, choosing at each step the constraint to minimize in a stochastic manner instead of dealing with all violations simultaneously. A notable recent application of this method is the mapping of the kinase inhibitor space for its broad profiling [72].…”

Section: Other Dimensionality Reduction Methodsmentioning

confidence: 99%