scholarly journals Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

2020 ◽  
Vol 2 (2) ◽  
pp. 29-38
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hilman Mujahid Purnama ◽  
Atika Nur Rahmawati
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang menerapkannya, baik di bidang komersil ataupun di bidang penegakan hukum. Pengenalan wajah merupakan sebuah sistem yang berfungsikan untuk mengidentifikasi berdasarkan ciri-ciri dari wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi. Pengenalan wajah dapat diterapkan pada sistem keamanan. Banyak metode yang dapat digunakan dalam aplikasi pengenalan wajah untuk keamanan sistem, namun pada artikel ini akan membahas tentang dua metode yaitu Two Dimensial Principal Component Analysis dan Kernel Fisher Discriminant Analysis dengan metode klasifikasi menggunakan K-Nearest Neigbor. Kedua metode ini diuji menggunakan metode cross validation. Hasil dari penelitian terdahulu terbukti bahwa sistem pengenalan wajah metode Two Dimensial Principal Component Analysis dengan 5-folds cross validation menghasilkan akurasi sebesar 88,73%, sedangkan dengan 2-folds validation akurasi yang dihasilkan sebesar 89,25%. Dan pengujian metode Kernel Fisher Discriminant dengan 2-folds cross validation menghasilkan akurasi rata rata sebesar 83,10%.

Apple Grading Using Principal Component Analysis and Kernel Fisher Discriminant Analysis Combined with NIR Spectroscopy

2013 ◽  
Vol 710 ◽  
pp. 529-533
Author(s):  
Xiao Hong Wu ◽  
Wen Jie Xu ◽  
Bin Wu ◽  
Sheng Wei Qiu
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Fisher Discriminant Analysis ◽  
Near Infrared Reflectance ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis ◽  
Separable Problems

Principal component analysis (PCA) and kernel Fisher discriminant analysis (KFDA) were applied to grade Fuji apples combined with near infrared reflectance (NIR) spectroscopy. Firstly, PCA was used to reduce the dimensionality of NIR spectra acquired by the Antaris II FT-NIR spectrophotometer on apples. Secondly, nonlinear discriminant information was extracted by kernel Fisher discriminant analysis (KFDA). Finally, the k-nearest neighbors algorithm with leave one out strategy was utilized to classify apple samples into two grades. LDA can only solve linearly separable problems, and it is not suitable in solving some nonlinear problems. But unlike LDA, KFDA can solve nonlinearly separable problems, and it projects data onto a high-dimensional feature space by the nonlinear mapping. Experimental results showed that KFDA achieved higher classification rate compared with LDA.

scholarly journals Fault Diagnosis Method on Polyvinyl Chloride Polymerization Process Based on Dynamic Kernel Principal Component and Fisher Discriminant Analysis Method

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Shu-zhi Gao ◽  
Xiao-feng Wu ◽  
Gui-cheng Wang ◽  
Jie-sheng Wang ◽  
Zi-qing Chai
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Polyvinyl Chloride ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Polymerization Process ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant

In view of the fact that the production process of Polyvinyl chloride (PVC) polymerization has more fault types and its type is complex, a fault diagnosis algorithm based on the hybrid Dynamic Kernel Principal Component Analysis-Fisher Discriminant Analysis (DKPCA-FDA) method is proposed in this paper. Kernel principal component analysis and Dynamic Kernel Principal Component Analysis are used for fault diagnosis of Polyvinyl chloride (PVC) polymerization process, while Fisher Discriminant Analysis (FDA) method was adopted to make failure data for further separation. The simulation results show that the Dynamic Kernel Principal Component Analyses to fault diagnosis of Polyvinyl chloride (PVC) polymerization process have better diagnostic accuracy, the Fisher Discriminant Analysis (FDA) can further realize the fault isolation, and the actual fault in the process of Polyvinyl chloride (PVC) polymerization production can be monitored by Dynamic Kernel Principal Component Analysis.

FAULT DIAGNOSIS BASED ON MULTI-SCALE CLASSIFICATION USING KERNEL FISHER DISCRIMINANT ANALYSIS AND GAUSSIAN MIXTURE MODEL AND K-NEAREST NEIGHBOR METHOD

2017 ◽  
Vol 79 (5-3) ◽  
Author(s):  
Norazwan Md Nor ◽  
Mohd Azlan Hussain ◽  
Che Rosmani Che Hassan
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Gaussian Mixture ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Multi Scale ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Effective fault monitoring, detection and diagnosis of chemical processes is important to ensure the consistency and high product quality, as well as the safety of the processes. Fault diagnosis problems can be considered as classification problems as these techniques have been proposed and greatly improved over the past few years. However, a chemical process is often characterized by large scale and non-linear behavior. When linear discriminant analysis is used for fault diagnosis in the system, a lot of incorrect diagnosis will occur. As solution, this paper presents a novel approach for feature extraction and classification framework in chemical process systems based on wavelet transformation and discriminant analysis. The proposed multi-scale kernel Fisher discriminant analysis (MSKFDA) method used the combination of kernel Fisher discriminant analysis (KFDA) and discrete wavelet transform (DWT) to improve the classification performance as compared to conventional approaches. A DWT is applied to extract the process dynamics at different scales by decomposed the data into multiple scales, analyzed by the KFDA and only dynamical characteristics with important information was reconstructed by inverse discrete wavelet transform (IDWT). Then, Gaussian mixture model (GMM) and K-nearest neighbor (KNN) method were individually applied for the fault classification using the output from the MSKFDA approach. These two classifiers are evaluated and compared based on their performance on the Tennessee Eastman process database. The proposed framework for GMM and KNN classifiers had achieved average classification accuracies of 84.72% and 82.00%, respectively, with the results show significant improvement over existing methods in fault detection and classification.

scholarly journals PERANCANGAN APLIKASI PENGENALAN CITRA WAJAH MENGGUNAKAN METODE COMPLETE KERNEL FISHER DISCRIMINANT

2019 ◽  
Vol 3 (1) ◽  
pp. 92
Author(s):  
Aninda Muliani ◽  
Suwandy Kosasih
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Fisher Linear Discriminant ◽  
Fisher Discriminant Analysis ◽  
Linear Discriminant ◽  
Fisher Discriminant

Abstrak - Pelacakan dan pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang dapat menerapkannya, baik di bidang komersial maupun bidang penegakan hukum. Teknik pengenalan wajah pada saat ini telah mengalami kemajuan yang sangat berarti, mengingat teknik pengenalan wajah ini merupakan bidang penelitian yang sangat dibutuhkan untuk berbagai bidang. Aplikasi face recognition pada saat ini banyak dikembangkan karena dapat diaplikasikan di berbagai bidang permasalahan seperti pengenalan kriminal, aplikasi keamanan, absensi, ataupun interaksi manusia dengan komputer. Banyak metode yang digunakan dalam aplikasi pengenalan wajah, salah satu diantaranya adalah metode Complete Kernel Fisher Discriminant(CKFD) yang merupakan pengembangan dari metode Kernel Principal Component Analysis (PCA) dan Fisher Discriminant Analysis (FDA). Metode Complete Kernel Fisher Discriminant (CKFD) memiliki dua keuntungan bila dibandingkan dengan Kernel Fisher Discriminant (KFD) sebelumnya. Pertama, implementasi algoritma ini dapat dibagi ke dalam dua fase, yaitu Kernel principal component analysis (KPCA) ditambah Fisher linear discriminant analysis (FLD) sehingga hasilnya lebih transparan dan lebih sederhana. Kedua, CKFD bisa membuat dua kategori informasi diskriminan sehingga hasilnya lebih kuat. Perancangan aplikasi ini bertujuan untuk implementasi sistem pengenalan wajah menggunakan metode Complete Kernel Fisher Discriminant (CKFD). Perancangan aplikasi pengenalan citra wajah ini dirancang menggunakan Visual Basic 2008, dimana sistem ini sudah bisa digunakan untuk pengenalan wajah dengan rata-rata 56,48% sehingga hasilnya dapat dikatakan tidak begitu akurat. Namun sistem ini perlu pengembangan lebih lanjut, agar bisa digunakan untuk mengenali citra wajah dengan lebih akurat, serta peningkatan ukuran dan resolusi gambar yang digunakan di atas 240x240 piksel. Kata Kunci - Sistem Pengenalan Wajah dan Metode Complete Kernel Fisher Discriminant (CKFD) Abstract - Face recognition is one of the important research fields and lately many applications can apply it, both in the commercial and law enforcement fields. Facial recognition techniques have now made significant progress, considering that face recognition techniques are a much-needed field of research for various fields. The face recognition application is currently being developed because it can be applied in various fields of problems such as criminal recognition, security applications, attendance, or human interaction with computers. Many methods are used in face recognition applications, one of which is the Complete Kernel Fisher Discriminant (CKFD) method which is the development of the Kernel Principal Component Analysis (PCA) method and Fisher Discriminant Analysis (FDA). The Complete Kernel Fisher Discriminant (CKFD) method has two advantages compared to the previous Kernel Fisher Discriminant (KFD). First, the implementation of this algorithm can be divided into two phases, namely Kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (FLD) so that the results are more transparent and simpler. Second, the CKFD can make two categories of discriminant information so that the results are stronger. The design of this application aims to implement face recognition systems using the Complete Kernel Fisher Discriminant (CKFD) method. The design of face image recognition application is designed using Visual Basic 2008, where this system can be used for face recognition with an average of 56.48% so the results can be said to be not very accurate. But this system needs further development, so that it can be used to recognize facial images more accurately, and increase the size and resolution of images used above 240x240 pixels. Keywords - Face Recognition System, and Method Complete Kernel Fisher Discriminant (CKFD)

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