• 한국통신학회논문지
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• 제38C권2호
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• pp.208-212
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• 2013

부류안 분산 행렬의 특이성 때문에 선형 판별 분석은 작은 표본 크기 문제에 쓰기에 알맞지 않다. 이에 선형 판별 분석을 확장하여 작은 표본 크기 문제에서 좋은 성능을 갖는 영 공간 기반 선형 판별 분석이 제안되었다. 이 논문에서는 라그랑지 기법을 바탕으로 하여, 영 공간 기반 선형 판별 분석을 써서 특징을 추출하는 문제를 선형 방정식 문제로 바꾸는 과정을 제안하였다.

• ETRI Journal
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• 제31권4호
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• pp.438-447
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• 2009

In this paper, a novel image projection technique for face recognition application is proposed which is based on linear discriminant analysis (LDA) combined with the relevance-weighted (RW) method. The projection is performed through 2-directional and 2-dimensional LDA, or $(2D)^2$LDA, which simultaneously works in row and column directions to solve the small sample size problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and an RW method is used in the within-class scatter matrix to weigh the information to resolve confusable data in these classes. This technique is called the relevance-weighted $(2D)^2$LDA, or RW$(2D)^2$LDA, which is used for a more accurate discriminant decision than that produced by the conventional LDA or 2DLDA. The proposed technique has been successfully tested on four face databases. Experimental results indicate that the proposed RW$(2D)^2$LDA algorithm is more computationally efficient than the conventional algorithms because it has fewer features and faster times. It can also improve performance and has a maximum recognition rate of over 97%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4502-4518
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• 2015

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method called local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the "divide-conquer" strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.

• Journal of the Korean Statistical Society
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• 제30권2호
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• pp.193-217
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• 2001

Despite of the rich literature in discriminant analysis, this complicated subject remains much to be explored. In this article, we study the theoretical foundation that supports Fisher's linear discriminant analysis (LDA) by setting up the classification problem under the dimension reduction framework as in Li(1991) for introducing sliced inverse regression(SIR). Through the connection between SIR and LDA, our theory helps identify sources of strength and weakness in using CRIMCOORDS(Gnanadesikan 1977) as a graphical tool for displaying group separation patterns. This connection also leads to several ways of generalizing LDA for better exploration and exploitation of nonlinear data patterns.

• 대한음성학회지:말소리
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• 제44호
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• pp.105-113
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• 2002

In Korean, each digit is monosyllable and some pairs are known to have high confusability, causing performance degradation of connected digit recognition systems. To improve the performance, in this paper, we employ various discriminant analyses (DA) including Linear DA (LDA), Weighted Pairwise Scatter LDA WPS-LDA), Heteroscedastic Discriminant Analysis (HDA), and Maximum Likelihood Linear Transformation (MLLT). We also examine several combinations of various DA for additional performance improvement. Experimental results show that applying any DA mentioned above improves the string accuracy, but the amount of improvement of each DA method varies according to the model complexity or number of mixtures per state. Especially, more than 20% of string error reduction is achieved by applying MLLT after WPS-LDA, compared with the baseline system, when class level of DA is defined as a tied state and 1 mixture per state is used.

• 한국정보통신학회논문지
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• 제22권1호
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• pp.125-130
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• 2018

선형판별분석(LDA) 기법은 특징벡터의 차원을 줄이거나 클래스 식별에 이용되는 통계적 분석 방법이다. 그러나 선형 분리가 불가능한 데이터 집합의 경우에는 비선형 함수를 이용하여 특징벡터를 고차원의 공간으로 사상(mapping) 시켜줌으로써 선형 분리가 가능하도록 만들 수 있는데, 이러한 기법을 일반화된 판별분석(GDA) 또는 커널판별분석(KDA) 기법이라고 한다. 본 연구에서는 인터넷에 공개되어 있는 능동소나 표적신호에 LDA 및 GDA 기법을 이용하여 표적식별 실험을 수행하고, 그 결과를 비교/분석하였다. 실험 결과 104개의 테스트 데이터에 대해 LDA 기법으로는 73.08% 인식률을 얻었으나 GDA 기법으로는 95.19%로 기존의 MLP 또는 커널 기반 SVM에 비해 나은 성능을 보였다.

• Communications for Statistical Applications and Methods
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• 제28권1호
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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 the Optical Society of Korea
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• 제12권2호
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• pp.88-92
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• 2008

This paper discusses a photon-counting linear discriminant analysis (LDA) with computational integral imaging (II). The computational II method reconstructs three-dimensional (3D) objects on the reconstruction planes located at arbitrary depth-levels. A maximum likelihood estimation (MLE) can be used to estimate the Poisson parameters of photon counts in the reconstruction space. The photon-counting LDA combined with the computational II method is developed in order to classify partially occluded objects with photon-limited images. Unknown targets are classified with the estimated Poisson parameters while reconstructed irradiance images are trained. It is shown that a low number of photons are sufficient to classify occluded objects with the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권3호
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• 한국콘텐츠학회논문지
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• 제5권6호
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• pp.41-48
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• 2005

이 논문은 얼굴인식을 수행하기 위해서 이미 잘 알려진 주성분 분석법과 선형판별 분석법에 레이디얼 기저 함수 신경망을 결합한 인식 알고리즘을 제시하였다. 입력된 원래의 얼굴영상은 주성분분석법을 통하여 차원을 줄인 고유 얼굴 가중치를 산출한다. 이 가중치 벡터를 선형판별 분석법의 입력데이터로 사용하여 선형판별분석의 변환행렬을 계산할 때 클래스 내의 분산행렬에서 특이점이 발생하지 않도록 하면서 특징벡터를 산출하여 인식을 수행하였다. 두 번째 시도에서는 선형판별분석법에 의해 생성된 특징벡터를 레이디얼 기저 함수 신경망에 입력하여 학습하고 얼굴인식을 수행하였다. ORL DB의 얼굴영상에 대해 실험한 결과 93.5%의 인식률을 얻을 수 있었다.