• Title/Summary/Keyword: one class classification

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Improved Face Recognition based on 2D-LDA using Weighted Covariance Scatter (가중치가 적용된 공분산을 이용한 2D-LDA 기반의 얼굴인식)

  • Lee, Seokjin;Oh, Chimin;Lee, Chilwoo
    • Journal of Korea Multimedia Society
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    • v.17 no.12
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    • pp.1446-1452
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    • 2014
  • Existing LDA uses the transform matrix that maximizes distance between classes. So we have to convert from an image to one-dimensional vector as training vector. However, in 2D-LDA, we can directly use two-dimensional image itself as training matrix, so that the classification performance can be enhanced about 20% comparing LDA, since the training matrix preserves the spatial information of two-dimensional image. However 2D-LDA uses same calculation schema for transformation matrix and therefore both LDA and 2D-LDA has the heteroscedastic problem which means that the class classification cannot obtain beneficial information of spatial distances of class clusters since LDA uses only data correlation-based covariance matrix of the training data without any reference to distances between classes. In this paper, we propose a new method to apply training matrix of 2D-LDA by using WPS-LDA idea that calculates the reciprocal of distance between classes and apply this weight to between class scatter matrix. The experimental result shows that the discriminating power of proposed 2D-LDA with weighted between class scatter has been improved up to 2% than original 2D-LDA. This method has good performance, especially when the distance between two classes is very close and the dimension of projection axis is low.

Binary classification by the combination of Adaboost and feature extraction methods (특징 추출 알고리즘과 Adaboost를 이용한 이진분류기)

  • Ham, Seaung-Lok;Kwak, No-Jun
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.49 no.4
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    • pp.42-53
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    • 2012
  • In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.

Solving Multi-class Problem using Support Vector Machines (Support Vector Machines을 이용한 다중 클래스 문제 해결)

  • Ko, Jae-Pil
    • Journal of KIISE:Software and Applications
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    • v.32 no.12
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    • pp.1260-1270
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    • 2005
  • Support Vector Machines (SVM) is well known for a representative learner as one of the kernel methods. SVM which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, SVM is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with a binary SVM. One-Per-Class (OPC) and All-Pairs have been applied to solve the face recognition problem, which is one of the multi-class problems, with SVM. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. In this paper, we introduce the output coding methods as an approach for extending binary SVM to multi-class SVM and propose new output coding schemes based on the Error-Correcting Output Codes (ECOC) which is a dominant theoretical foundation of the output coding methods. From the experiment on the face recognition, we give empirical results on the properties of output coding methods including our proposed ones.

An Analysis of Elementary School Teachers' Difficulties in the STEAM Class (융합인재교육(STEAM) 수업에서 초등교사들이 겪는 어려움 분석)

  • Lee, Jeong-Min;Shin, Young-Joon
    • Journal of Korean Elementary Science Education
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    • v.33 no.3
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    • pp.588-596
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    • 2014
  • The purpose of this study is to provide basic data for improving the STEAM class by examining elementary school teachers' difficulties in the STEAM class and discussing solutions. For this research, 25 elementary school teachers in Seoul City and Gyeonggi-do were asked to write their difficulties in the STEAM class in the open-ended questionnaires. After classification of the collected data, an in-depth interview was conducted with one in-service elementary school teacher who is richly experienced in STEAM education to find solutions for each type of difficulties. The study result showed that most of elementary school teachers had difficulties in the STEAM class, due to selection of integrated subjects, production of teaching devices and materials, guidance of group activities, reorganization of the curriculum, assessment and uncooperative co-teachers. One teacher that participated in the interview to discuss solutions for teachers' difficulties was solving the problems in various ways. She said that many of her solutions came from her experience and also, knowledge obtained through a participation in the STEAM training or opportunities to share information with other teachers who belong to the STEAM research institution, was highly helpful.

Improved Feature Extraction of Hand Movement EEG Signals based on Independent Component Analysis and Spatial Filter

  • Nguyen, Thanh Ha;Park, Seung-Min;Ko, Kwang-Eun;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.4
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    • pp.515-520
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    • 2012
  • In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).

On the Use of Modified Adaptive Nearest Neighbors for Classification (수정된 적응 최근접 방법을 활용한 판별분류방법에 대한 연구)

  • Maeng, Jin-Woo;Bang, Sung-Wan;Jhun, Myoung-Shic
    • The Korean Journal of Applied Statistics
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    • v.23 no.6
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    • pp.1093-1102
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    • 2010
  • Even though the k-Nearest Neighbors Classification(KNNC) is one of the popular non-parametric classification methods, it does not consider the local features and class information for each observation. In order to overcome such limitations, several methods have been developed such as Adaptive Nearest Neighbors Classification(ANNC) and Modified k-Nearest Neighbors Classification(MKNNC). In this paper, we propose the Modified Adaptive Nearest Neighbors Classification(MANNC) that employs the advantages of both the ANNC and MKNNC. Through a real data analysis and a simulation study, we show that the proposed MANNC outperforms other methods in terms of classification accuracy.

Effect of Prior Probabilities on the Classification Accuracy under the Condition of Poor Separability

  • Kim, Chang-Jae;Eo, Yang-Dam;Lee, Byoung-Kil
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.26 no.4
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    • pp.333-340
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    • 2008
  • This paper shows that the use of prior probabilities of the involved classes improve the accuracy of classification in case of poor separability between classes. Three cases of experiments are designed with two LiDAR datasets while considering three different classes (building, tree, and flat grass area). Moreover, random sampling method with human interpretation is used to achieve the approximate prior probabilities in this research. Based on the experimental results, Bayesian classification with the appropriate prior probability makes the improved classification results comparing with the case of non-prior probability when the ratio of prior probability of one class to that of the other is significantly different to 1.0.

Change Detection in Land-Cover Pattern Using Region Growing Segmentation and Fuzzy Classification

  • Lee Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.21 no.1
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    • pp.83-89
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    • 2005
  • This study utilized a spatial region growing segmentation and a classification using fuzzy membership vectors to detect the changes in the images observed at different dates. Consider two co-registered images of the same scene, and one image is supposed to have the class map of the scene at the observation time. The method performs the unsupervised segmentation and the fuzzy classification for the other image, and then detects the changes in the scene by examining the changes in the fuzzy membership vectors of the segmented regions in the classification procedure. The algorithm was evaluated with simulated images and then applied to a real scene of the Korean Peninsula using the KOMPSAT-l EOC images. In the expertments, the proposed method showed a great performance for detecting changes in land-cover.

Performance Improvement of Multilayer Perceptrons with Increased Output Nodes (다층퍼셉트론의 출력 노드 수 증가에 의한 성능 향상)

  • Oh, Sang-Hoon
    • The Journal of the Korea Contents Association
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    • v.9 no.1
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    • pp.123-130
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    • 2009
  • When we apply MLPs(multilayer perceptrons) to pattern classification problems, we generally allocate one output node for each class and the index of output node denotes a class. On the contrary, in this paper, we propose to increase the number of output nodes per each class for performance improvement of MLPs. For theoretical backgrounds, we derive the misclassification probability in two class problems with additional outputs under the assumption that the two classes have equal probability and outputs are uniformly distributed in each class. Also, simulations of 50 isolated-word recognition show the effectiveness of our method.

A Study on the Improvement of Classification System in Advertising Field of KDC (KDC 광고분야의 분류체계 개선에 관한 연구)

  • Kim, Jeong-Hyen;Bae, Joo-Yun
    • Journal of the Korean Society for information Management
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    • v.22 no.4 s.58
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    • pp.5-22
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    • 2005
  • As the development of advertising industry and media the research about an advertisement get accomplished. As the result information resources called on the advertising materials are on an Increasing trend. However, it looks into the classification system In advertising field of KDC, the problems are as the follows : (1) the classification items are too incomplete, (2) the main class is badly arranged. The reason have no regard for the correlation with a science. So, it gives rise to confusion to the librarian and user. The purpose of the study is to present the improvement plan on the classification system in advertising field of KDC. In order to build the improvement plan, the four steps are utilized. The first step Is to investigate the characteristic of sciences on advertising and a type. The second one is to survey the current status of the library classification as KDC, NDC, DDC, and LCC. The third one is to analyse the classification system of library and web site on the advertising. The forth one is to grasp the problems on the classification system In advertising field of KDC.