Protein sub-cellular localization based on noise-intensity-weighted linear discriminant analysis and an improved k-nearest-neighbor classifier

2016 ◽  
Author(s):  
Zhenfeng Lei ◽  
Shunfang Wang ◽  
Dongshu Xu
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Noise Intensity ◽  
Cellular Localization ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

scholarly journals Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis dan k Nearest Neighbor

2017 ◽  
Vol 9 (1) ◽  
pp. 1-9
Author(s):  
Fandiansyah Fandiansyah ◽  
Jayanti Yusmah Sari ◽  
Ika Putri Ningrum
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbor ◽  
Experimental Result ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

Face recognition is one of the biometric system that mostly used for individual recognition in the absent machine or access control. This is because the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. Feature extraction and classification are the key to face recognition, as they are to any pattern classification task. In this paper, we describe a face recognition method based on Linear Discriminant Analysis (LDA) and k-Nearest Neighbor classifier. LDA used for feature extraction, which directly extracts the proper features from image matrices with the objective of maximizing between-class variations and minimizing within-class variations. The features of a testing image will be compared to the features of database image using K-Nearest Neighbor classifier. The experiments in this paper are performed by using using 66 face images of 22 different people. The experimental result shows that the recognition accuracy is up to 98.33%. Index Terms—face recognition, k nearest neighbor, linear discriminant analysis.

Linear Discriminant Analysis for Prediction of Group Membership: A User-Friendly Primer

2019 ◽  
Vol 2 (3) ◽  
pp. 250-263 ◽  
Author(s):  
Peter Boedeker ◽  
Nathan T. Kearns
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Best Practice ◽  
Nearest Neighbor ◽  
Multinomial Logistic Regression ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Prior Specification

In psychology, researchers are often interested in the predictive classification of individuals. Various models exist for such a purpose, but which model is considered a best practice is conditional on attributes of the data. Under certain conditions, linear discriminant analysis (LDA) has been shown to perform better than other predictive methods, such as logistic regression, multinomial logistic regression, random forests, support-vector machines, and the K-nearest neighbor algorithm. The purpose of this Tutorial is to provide researchers who already have a basic level of statistical training with a general overview of LDA and an example of its implementation and interpretation. Decisions that must be made when conducting an LDA (e.g., prior specification, choice of cross-validation procedures) and methods of evaluating case classification (posterior probability, typicality probability) and overall classification (hit rate, Huberty’s I index) are discussed. LDA for prediction is described from a modern Bayesian perspective, as opposed to its original derivation. A step-by-step example of implementing and interpreting LDA results is provided. All analyses were conducted in R, and the script is provided; the data are available online.

Crime Detection with ICA and Artificial Intelligent Approach

2013 ◽  
Vol 816-817 ◽  
pp. 616-622
Author(s):  
Ahmad Kadri Junoh ◽  
Muhammad Naufal Mansor ◽  
Alezar Mat Ya'acob ◽  
Farah Adibah Adnan ◽  
Syafawati Ab. Saad ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Independent Component Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Violent Crimes ◽  
Artificial Intelligent ◽  
Linear Discriminant ◽  
Crime Detection ◽  
Intelligent Approach

The Rise of Crime in Malaysia reported that violent crimes comprised only 10% of reported crimes each year and the majority of crimes, 90%, were classified as property crimes. However, the ratio of police to population is 3.6 officers to 1,000 citizens in Malaysia. This lack of manpower sources ratios alone are not a comprehensive afford of crime fighting capabilities. Thus, we proposed an Artificial Intelligent Techniques to determine the behaviour of the burglar with Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and k Nearest Neighbor (k-NN) Classifier. This system provided a good justification as a monitoring supplementary tool for the Malaysian police arm forced.

PENGGUNAAN METODE LINEAR DISCRIMINANT ANALYSIS UNTUK PENGENALAN WAJAH DENGAN MEMBANDINGKAN BANYAKNYA DATA LATIH

2021 ◽  
Vol 26 (1) ◽  
pp. 25-34
Author(s):  
Rifki Kosasih
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principle Component Analysis ◽  
Nearest Neighbor ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Principle Component

Pengenalan wajah sangat dibutuhkan dalam sistem keamanan rumah karena dapat membantu mengetahui siapa saja yang sudah memasuki area rumah. Salah satu metode yang dapat digunakan dalam pengenalan wajah adalah metode Principle Component Analysis (PCA). Akan tetapi, metode PCA kurang optimal dalam melakukan pemisahan antar kelas. Oleh karena itu pada penelitian ini digunakan metode lain yang dapat melakukan pemisahan antar kelas secara optimal seperti metode Linear Discriminant Analysis (LDA). Data yang digunakan sebanyak 400 data citra wajah dengan komposisi 40 orang dengan tiap orang memiliki 10 citra wajah dengan berbagai ekspresi. Pada penelitian ini diusulkan untuk memperhatikan banyaknya data latih yang digunakan. Banyaknya citra wajah tiap orang yang digunakan untuk data latih adalah 5, 6, 7, 8 dan 9 citra wajah per orang. Selanjutnya dilakukan ekstraksi fitur dengan menggunakan metode LDA. Selanjutnya dilakukan klasifikasi terhadap fitur-fitur yang telah diperoleh dengan menggunakan metode K Nearest Neighbor (KNN). Berdasarkan hasil penelitian diperoleh bahwa tingkat akurasi terbesar yaitu sebesar 97,5% yang terjadi saat banyaknya citra data latih tiap orang adalah 9 dan banyaknya tetangga (K) adalah 1.

Sign in / Sign up

Export Citation Format

Share Document