scholarly journals Training Data Reduction and Classification Based on Greedy Kernel Principal Component Analysis and Fuzzy C-means Algorithm

2013 ◽  
Author(s):  
Xiaofang Liu ◽  
Chun Yang
Keyword(s):  
Principal Component Analysis ◽  
Data Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Training Data ◽  
Kernel Principal Component Analysis ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Training Data Reduction and Classification Based on Greedy Kernel Principal Component Analysis and Fuzzy C-Means Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 2390-2394
Author(s):  
Xiao Fang Liu ◽  
Chun Yang
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Computational Complexity ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Training Data ◽  
Kernel Principal Component Analysis ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

Nonlinear feature extraction used standard Kernel Principal Component Analysis (KPCA) method has large memories and high computational complexity in large datasets. A Greedy Kernel Principal Component Analysis (GKPCA) method is applied to reduce training data and deal with the nonlinear feature extraction problem for training data of large data in classification. First, a subset, which approximates to the original training data, is selected from the full training data using the greedy technique of the GKPCA method. Then, the feature extraction model is trained by the subset instead of the full training data. Finally, FCM algorithm classifies feature extraction data of the GKPCA, KPCA and PCA methods, respectively. The simulation results indicate that the feature extraction performance of both the GKPCA, and KPCA methods outperform the PCA method. In addition of retaining the performance of the KPCA method, the GKPCA method reduces computational complexity due to the reduced training set in classification.

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.

Watermelon Ripeness Detection via Extreme Learning Machine with Kernel Principal Component Analysis Based on Acoustic Signals

2019 ◽  
Vol 33 (08) ◽  
pp. 1951002 ◽  
Author(s):  
Yinghao Zhang ◽  
Xiaoyan Deng ◽  
Zhou Xu ◽  
Peipei Yuan
Keyword(s):  
Principal Component Analysis ◽  
Linear Combination ◽  
Extreme Learning Machine ◽  
Principal Component ◽  
Acoustic Signals ◽  
Component Analysis ◽  
Training Sample ◽  
Training Data ◽  
Kernel Principal Component Analysis ◽  
Learning Machine

Many investigations have proved that the acoustics method is intuitive and effective for determining watermelon ripeness. The objective of this work is to drive a new robust acoustics classification scheme KPCA-ELM, which is based on the kernel principal component analysis (KPCA) and extreme learning machine (ELM). Acoustic signals are sampled by a microphone from unripe, ripe and over-ripe watermelon samples, which are randomly divided into two sample sets for training and testing. A set of basic signals is first obtained via KPCA of the training sample. Thus, any given signal can be represented as a linear combination of basis signals, and the coefficients of linear combination are extracted as the features of a signal. Corresponding to the unripe, ripe and over-ripe watermelons, a three-class ELM identification model is constructed based on the training data. The scheme presented in this paper is tested with the testing sample and an accuracy of 92% is achieved. To further evaluate the scheme performance, a comparison of ELM and SVM is conducted in terms of the classification results. The results reveal that the proposed scheme can classify faster than SVM, while ELM is better than SVM in accuracy.

scholarly journals Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6370
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
David Mba ◽  
Xiaochuan Li ◽  
Tat-Hean Gan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Water Purification ◽  
Principal Component ◽  
Temporal Correlation ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Original Dataset ◽  
Monitoring Process ◽  
Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

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