• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.848-850
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• 2007

본 논문은 치아의 모양, 크기 및 턱의 모양 등과 같은 치아의 기하학적인 특징들을 사용하여 치아의 성 변별시스템에 PCA기법과 LDA기법을 각각 적용하고 두 기법을 비교분석한다. PCA기법과 LDA기법은 생체인식을 위한 주요 매핑기법으로 알려져 있다. PCA분석 기법을 적용하여 성변별의 결과 76%의 인식률이 획득되었으며, LDA분석기법은 66%의 인식률이 획득되었다. 본 연구의 결과로부터 PCA기법은 치아의 성변별에 있어 LDA기법보다 우수한 성능을 제공함을 확인할 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.17-22
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• 1999

In this paper we propose a new mixed method of LDA and tri-tone model to predict Korean prosodic break indices(PBI) for a given utterance. PBI can be used as an important cue of syntactic discontinuity in continuous speech recognition(CSR). The model consists of three steps. At the first step, PBI was predicted with the information of syllable and pause duration through the linear discriminant analysis (LDA) method. At the second step, syllable tone information was used to estimate PBI. In this step we used vector quantization (VQ) for coding the syllable tones and PBI is estimated by tri-tone model. In the last step, two PBI predictors were integrated by a weight factor. The proposed method was tested on 200 literal style spoken sentences. The experimental results showed 72% accuracy.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.21-37
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• 2021

There is a direct connection between linear discriminant analysis (LDA) and linear regression since the direction vector of the LDA can be obtained by the least square estimation. The connection motivates the penalized LDA when the model is high-dimensional where the number of predictive variables is larger than the sample size. In this paper, we study the penalized LDA for a class of penalties, called the moderately clipped LASSO (MCL), which interpolates between the least absolute shrinkage and selection operator (LASSO) and minimax concave penalty. We prove that the MCL penalized LDA correctly identifies the sparsity of the Bayes direction vector with probability tending to one, which is supported by better finite sample performance than LASSO based on concrete numerical studies.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.923-933
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• 2007

In this paper, we present a method for performance enhancement of the marker detection system by using SVM(Support Vector Machine) and LDA(Linear Discriminant Analysis). It converts the input image to a binary image and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds quadrangle by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted quadrangle into exact squares by using the warping technique and scale transformation. It extracts feature vectors from the square image by using principal component analysis. It then checks if the square image is a marker image or a non-marker image by using a SVM classifier. After that, it computes feature vectors by using LDA for the extracted marker images. And it calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the proposed method achieves enhancement of recognition rate with smaller feature vectors by using LDA and it can decrease false detection errors by using SVM.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.15-33
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• 2018

The main purpose of this study was to identify the topics and research trends of international cadastral research using LDA. To achieve this goal, I reviewed the literature on LDA and international cadastral study and formulated four research questions that are topics of cadastral researchers, distribution of topics, the most influential topics and changes of topics over time. To answer these research questions, I analyzed 370 papers published in the FIG Peer Review Journal between January 1, 2008, and October 31, 2017, using LDA. As a result of the analysis, I confirmed that there are twelve major topics in international cadastral research. And the most influential topic of these topics was identified as topic 2(cadastral information systems), and topic 5(land development and land administration) was also confirmed as playing an important role in the overall document. These two topics have been the most popular topics whose trendlines have been very active over the past decade and will play a leading role in future cadastral research.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.367-371
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• 2006

Multilayer percepteon has the advantage of learning their optimal parameters and efficiency. However, MLP shows some drawbacks when dealing with high dimensional data within the input space. Also, it Is very difficult to find the optimal parameters when the input data are highly correlated such as large scale face dataset. In this paper, we propose a novel technique for face recognition based on LDA and local MLP. To resolve the main drawback of MLP, we calculate the reduced features by LDA in advance. And then, we construct a local MLP per group consisting of subset of facedatabase to find its optimal learning parameters rather than using whole faces. Finally, we designed the face recognition system combined with the local MLPs. From various experiments, we obtained better classification performance in comparison with the results produced by conventional methods such as PCA and LDA.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.9
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• pp.935-944
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• 2006

EMG pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study is to develop an efficient feature projection method for EMC pattern recognition. To this end, we propose a linear supervised feature projection that utilizes linear discriminant analysis (LDA). We first perform wavelet packet transform (WPT) to extract the feature vector from four channel EMC signals. For dimensionality reduction and clustering of the WPT features, the LDA incorporates class information into the learning procedure, and finds a linear matrix to maximize the class separability for the projected features. Finally, the multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of LDA for the WPT features, we compare LDA with three other feature projection methods. From a visualization and quantitative comparison, we show that LDA has better performance for the class separability, and the LDA-projected features improve the classification accuracy with a short processing time. We implemented a real-time pattern recognition system for a multifunction myoelectric hand. In experiment, we show that the proposed method achieves 97.2% recognition accuracy, and that all processes, including the generation of control commands for myoelectric hand, are completed within 97 msec. These results confirm that our method is applicable to real-time EMG pattern recognition far myoelectric hand control.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.423-432
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• 2022

This paper analyzes a total of 10,599 papers related to COVID-19 from January 2020 to July 2022 collected from the KCI site using LDA topic modeling so that academic researchers can understand the overall research trend. The results of LDA topic modeling are analyzed by major research categories so that academic researchers can easily figure out topics in their research fields. Then, the detailed research category information in which a lot of research is done by topic is analyzed. It is very important for academic researchers to understand the trend of research topics over time. Therefore, in this paper, the trend of topics is analyzed and presented using time series decomposition.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.131-136
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• 2020

Wake Up Word (WUW) is a short utterance used to convert speech recognizer to recognition mode. The WUW defined by the user who actually use the speech recognizer is called user-defined WUW. In this paper, to recognize user-defined WUW, we construct traditional Gaussian Mixture Model-Hidden Markov Model (GMM-HMM), Linear Discriminant Analysis (LDA)-GMM-HMM and LDA-Deep Neural Network (DNN)-HMM based system and compare their performances. Also, to improve recognition accuracy of the WUW system, a threshold method is applied to each model, which significantly reduces the error rate of the WUW recognition and the rejection failure rate of non-WUW simultaneously. For LDA-DNN-HMM system, when the WUW error rate is 9.84 %, the rejection failure rate of non-WUW is 0.0058 %, which is about 4.82 times lower than the LDA-GMM-HMM system. These results demonstrate that LDA-DNN-HMM model developed in this paper proves to be highly effective for constructing user-defined WUW recognition system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.1161-1164
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• 2005

본 논문에서는 얼굴 영상에서 눈동자와 입술을 효과적으로 인식하는 방법을 제안하였다. 색 정보를 기반으로 LDA를 이용하여 입술 영역을 찾았다. 눈동자와 흰자위로 구성되는 눈의 형태적인 특징과 눈동자와 눈썹 사이의 관계를 반영하는 평가함수를 정의하여 눈동자를 인식하였다. 입술에서의 밝기차이를 기반으로 가중치 벡터를 정의하여 위 입술과 아래 입술 사이의 경계선을 찾고 입술과 인접한 피부와의 밝기 차이를 이용하여 입술의 양 끝점 및 위와 아래의 끝점을 찾았다. 여러 영상에 대한 실험 결과 좋은 결과를 얻었다.