Robust Recommendation Algorithm Based on Kernel Principal Component Analysis and Fuzzy C-means Clustering

2018 ◽  
Vol 23 (2) ◽  
pp. 111-119 ◽  
Author(s):  
Huawei Yi ◽  
Zaiseng Niu ◽  
Fuzhi Zhang ◽  
Xiaohui Li ◽  
Yajun Wang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Recommendation Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Failure Prediction and Wear State Evaluation of Power Shift Steering Transmission

2015 ◽  
Vol 741 ◽  
pp. 183-187 ◽  
Author(s):  
Yong Liu ◽  
Biao Ma ◽  
Yu Yan ◽  
Chang Song Zheng
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Atomic Emission ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
State Evaluation ◽  
Power Shift ◽  
Fuzzy C Means Clustering ◽  
Friction Surfaces

Within the vehicle transmission, the friction surfaces of mechanical parts were consecutively worn-out and ultimately up to the degradation failures. For assessing the wear progress effectively, wear particles should be generally monitored by measuring the element concentration through Atomic emission (AE) spectroscopy. Herein, the spectral data sampled from life-cycle test has been processed by both the Principal Component Analysis (PCA) and further Kernel Principal Component Analysis (KPCA). Results show that KPCA acts more effectively in variable-dimensions reduction due to fewer principle components and higher cumulative contributing rate. To detect the threshold point at where the wear-stage upgraded, the Fuzzy C-means clustering algorithm was applied to process the eigenvalues of principle components. Furthermore, it is demonstrated that the principle components relate to the worn-out state of friction pairs or transmission parts. In general, the introduction of KPCA has contributed to assess the wear-stage at where the machine situates and the accurate worn-out state of various transmission parts.

On Kernel Fuzzy c-Means for Data with Tolerance Using Explicit Mapping for Kernel Data Analysis

2012 ◽  
Vol 16 (1) ◽  
pp. 162-168
Author(s):  
Yuchi Kanzawa ◽  
◽  
Yasunori Endo ◽  
Sadaaki Miyamoto ◽  
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Dimensional Space ◽  
Principal Component ◽  
Component Analysis ◽  
Inner Product ◽  
Kernel Principal Component Analysis ◽  
Common Assumption ◽  
Fuzzy C Means ◽  
Higher Dimensional

While explicit mapping is generally unknown for kernel data analysis, its inner product should be known. Although we proposed a kernel fuzzy c-means algorithm for data with tolerance, cluster centers and tolerance in higher dimensional space have not been seen. Contrary to this common assumption, explicit mapping has been introduced and the situation of kernel fuzzy c-means in higher dimensional space has been described via kernel principal component analysis using explicit mapping. In this paper, cluster centers and the tolerance of kernel fuzzy c-means for data with tolerance are described via kernel principal component analysis using explicit mapping.

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