Semisupervised local preserving embedding algorithm based on maximum margin criterion for large‐scale data streams

Tan, Chao; Ji, Genlin

doi:10.1002/cpe.4246

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…And the ONPP is taken as an example to design a novel sparse subspace learning framework [52]. In [58][59][60][61][62], some supervised and semisupervised dimensionality reduction methods based on NPE are proposed. NPE, as a manifold learning method, is a kind of linear approximation of LLE by replacing the nonlinear mapping relation to achieve dimensionality reduction [53,63].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

Dong

Ding

et al. 2018

Shock and Vibration

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In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT). In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD) was proposed to select fault sensitive features. Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL), was proposed to realize low-dimensional representations for high-dimensional feature space. Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure. The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system.

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Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

Dong

Ding

et al. 2018

Shock and Vibration

View full text Add to dashboard Cite

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“…In the field of machine learning, feature extraction is one of the most important preprocessing in data classification for its effectiveness, and now it has attracted much extensive attention for large‐scale data stream preprocessing step, especially in the era of Big Data. Motivated by the advantages of unsupervised and supervised feature extraction, which are two desirable and promising characteristics for dimension reduction, Tan et al propose a new Semi‐Supervised Local Preserving Embedding Algorithm based on Maximum Margin Criterion (SLPE/MMC). The proposed algorithm has effectively taken advantage of sample's supervised information, and keeps the geometry structure and the class discrimination information of the manifold.…”

mentioning

confidence: 99%

New trends and innovative methods in cloud computing and big data

Dong

Shen

2017

Concurrency and Computation

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New trends and innovative methods in cloud computing and big dataWith the rapid development of network technology in recent years, more and more innovative computing models and related applications appear, such as mobile device based cloud computing, social network, intelligent manufacturing and e-business, et al. Under these situations, massive data will be generated, stored and processed under various circumstances and different requirement. 1 In order to effectively store and analyze the big data based on cloud computing architecture to realize intelligent network-based applications, the new methodologies and technologies for both cloud computing and big data need to be proposed and developed. • Cloud storage system design and implementation,• Mobile cloud computing security and privacy protection,• Big data analysis and processing technique, and• Big data analysis applications.The secure data access and access performance are two important concerns in the management and storage of large-scale data in enterprises.However, current solutions have the following drawbacks: (1) do not support fine-grained access control and cannot meet the strict secure data access requirements of enterprises, and (2) suffer from the unpredictable access latency. To solve these drawbacks, Dong et al 2 propose an enterprise-oriented cloud storage system, called Frostor, which addresses the secure data access issue through a user account and IP-based fine-grained access control mechanism, and guarantees the access performance via a two-level performance optimization mechanism.With the prevalence of smartphones, the number of Android malware shows explosive growth. As malicious apps may steal users 0 sensitive data and even money from mobile and bank accounts, it is important to detect potential malicious behaviors so as to block them. To achieve this goal, Yang et al 3 propose a dynamic behavior inspection and analysis framework for malicious behavior detection. An automated app behavior inspection platform is developed to install and inspect massive samples so as to collect apps 0 dynamic behavior Q3 records. Then these records are exploited to train a string subsequence kernel-based SVM model to classify benign and malicious behaviors.In the field of machine learning, feature extraction is one of the most important preprocessing in data classification for its effectiveness, and now it has attracted much extensive attention for large-scale data stream preprocessing step, especially in the era of Big Data. Motivated by the advantages of unsupervised and supervised feature extraction, which are two desirable and promising characteristics for dimension reduction, Tan et al 4 propose a new Semi-Supervised Local Preserving Embedding Algorithm based on Maximum Margin Criterion (SLPE/MMC). The proposed algorithm has effectively taken advantage of sample 0 s supervised information, and keeps the geometry structure and the class discrimination information of the manifold.Recent advances of recommender systems have proven that social relatio...

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Analysis and Prediction of the Leaching Process of Ionic Rare Earth: A Data Mining Study with Scarce Data

Zhang,

Yang,

Guo

et al. 2024

Minerals

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To unveil the impact of each condition variable on the leaching efficiency index during the heap leaching process of rare earth ore and establish a prediction model for leaching conditions and efficiency, common parameters in the heap leaching process of rare earth ore were selected. In addition, the pilot-scale test data were collected over 50 days. Based on the collected data, the Ordinary Least Squares (OLS) linear regression method was used for fitting analysis to determine each variable’s influence on the change in leaching efficiency. The results indicated a linear relationship between the flow rate of the leaching solution and leaching efficiency. In contrast, no obvious linear relationship was observed between other condition variables and leaching efficiency. Spearman’s rank correlation coefficient was calculated to analyze the nonlinear correlation between the abovementioned variables and the leaching efficiency index. The correlation coefficients were found to be −0.78, 0.88, −0.93, −0.53, 0.71, and −0.93 for ammonium content in the leaching agent, pH of the leaching agent, rare earth content, ammonium content in the leaching solution, pH of the leaching solution, and the flow rate of the leaching solution, respectively. This suggests that the flow rate of the leaching solution, rare earth content, and pH of the leaching agent significantly influence leaching efficiency, thus affecting the rare earth leaching efficiency index. Based on the correlation analysis results of leaching conditions and efficiency, a dataset with limited data trained by the common Ordinary Least Squares model, linear regression model, random forest model, and support vector machine regression model was selected to develop a prediction model for the leaching process data. The results indicated that the random forest model had the lowest mean square error of 7.47 among the four models and the coefficient of determination closest to 1 (0.99). This model can effectively analyze and predict condition variables’ data and leaching efficiency index in the heap leaching process of rare earth ore, with a prediction accuracy exceeding 90%, thus providing intelligent guidance for the heap leaching process of rare earth ores.

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Semisupervised local preserving embedding algorithm based on maximum margin criterion for large‐scale data streams

Cited by 3 publications

References 37 publications

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

New trends and innovative methods in cloud computing and big data

Analysis and Prediction of the Leaching Process of Ionic Rare Earth: A Data Mining Study with Scarce Data

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