• 대한전자공학회논문지SP
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• 제38권6호
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• pp.608-616
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• 2001

본 논문은 K-L 변환을 기반으로 한 Fisherface 알고리즘과 fixed graph matching (FGM) 방법을 이용하여 보다 효율적인 얼굴 인식 방법을 제안하고자 한다. 동적 링크 구조 방법 중에 하나인 elastic graph matching (EGM)은 얼굴의 모양 정보뿐만 아니라, 영상 픽셀의 그레이 정보를 동시에 이용하는 하며, 클래스를 구분하는 방법인 Fisherface 알고리즘은 빛의 방향 및 얼굴 표정과 같은 영상의 변화에 대해 강인하다고 알려져 있다. 위의 두 방법으로부터 제안한 알고리즘에서는 영상 그래프의 각 노드에 대해 Fisherface방법을 적용함으로써 레이블된 그래프 벡터의 차원을 줄일 뿐만 아니라 효율적으로 클래스를 구분하기 위한 특징 벡터를 제공한다. 그럼으로써 기존의 EGM 방법에 비해 인식 속도 면에서 상당한 향상 결과를 얻을 수 있었다. 특히, Olivetti Research Laboratory (ORL) 데이터베이스와 Yale 대학 데이터베이스에 대해 실험한 결과 제안한 얼굴 인식 알고리즘은 hold-out 방법에 의한 실험 결과, 평균 90.1%로 기존의 한 방법만을 사용한 것보다 높은 인식률을 보였다.

• 제어로봇시스템학회논문지
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• 제21권6호
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• pp.565-570
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• 2015

Supervised machine learning has become popular in discovering context descriptions from sensor data. However, collecting a large amount of labeled training data in order to guarantee good performance requires a great deal of expense and time. For this reason, semi-supervised learning has recently been developed due to its superior performance despite using only a small number of labeled data. In the existing semi-supervised learning algorithms, unlabeled data are used to build a graph Laplacian in order to represent an intrinsic data geometry. In this paper, we represent the unlabeled data as the spatial-temporal dataset by considering smoothly moving objects over time and space. The developed algorithm is evaluated for position estimation of a smartphone-based robot. In comparison with other state-of-art semi-supervised learning, our algorithm performs more accurate location estimates.

• Management Science and Financial Engineering
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• 제16권1호
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• pp.81-117
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• 2010

Semi-supervised learning incorporates unlabeled examples, whose labels are unknown, as well as labeled examples into learning process. Although transductive support vector machine (TSVM), one of semi-supervised learning models, was proposed about a decade ago, its application to large-scaled data has still been limited due to its high computational complexity. Our previous research addressed this limitation by introducing a branch-and-bound algorithm for finding an optimal solution to TSVM. In this paper, we propose three new techniques to enhance the performance of the branch-and-bound algorithm. The first one tightens min-cut bound, one of two bounding strategies. Another technique exploits a graph-based approximation to a support vector machine problem to avoid the most time-consuming step. The last one tries to fix the labels of unlabeled examples whose labels can be obviously predicted based on labeled examples. Experimental results are presented which demonstrate that the proposed techniques can reduce drastically the number of subproblems and eventually computational time.

• 한국경영과학회지
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• 제31권2호
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• pp.69-85
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• 2006

Transductive Support Vector Machine(TSVM) is one of semi-supervised learning algorithms which exploit the domain structure of the whole data by considering labeled and unlabeled data together. Although it was proposed several years ago, there has been no efficient algorithm which can handle problems with more than hundreds of training examples. In this paper, we propose an efficient branch-and-bound algorithm which can solve large-scale TSVM problems with thousands of training examples. The proposed algorithm uses two bounding techniques: min-cut bound and reduced SVM bound. The min-cut bound is derived from a capacitated graph whose cuts represent a lower bound to the optimal objective function value of the dual problem. The reduced SVM bound is obtained by constructing the SVM problem with only labeled data. Experimental results show that the accuracy rate of TSVM can be significantly improved by learning from the optimal solution of TSVM, rather than an approximated solution.

• Kyungpook Mathematical Journal
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• 제56권1호
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• pp.41-46
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• 2016

A graph G of order n has prime cordial labeling if its vertices can be assigned the distinct labels 1, $2{\cdots}$, n such that if each edge xy in G is assigned the label 1 in case the labels of x and y are relatively prime and 0 otherwise, then the number of edges labeled with 0 and the number of edges labeled with 1 differ by at most 1. In this paper, we give a complete characterization of complete graphs which are prime cordial and we give a prime cordial labeling of the closed helm ${\bar{H}}_n$, and present a new way of prime cordial labeling of $P^2_n$. Finally we make a correction of the proof of Theorem 2.5 in [12].

• Journal of applied mathematics & informatics
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• 제33권1_2호
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• pp.101-110
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• 2015

A Total Mean Cordial labeling of a graph G = (V, E) is a function $f:V(G){\rightarrow}\{0,1,2\}$ such that $f(xy)={\Large\lceil}\frac{f(x)+f(y)}{2}{\Large\rceil}$ where $x,y{\in}V(G)$, $xy{\in}E(G)$, and the total number of 0, 1 and 2 are balanced. That is ${\mid}ev_f(i)-ev_f(j){\mid}{\leq}1$, $i,j{\in}\{0,1,2\}$ where $ev_f(x)$ denotes the total number of vertices and edges labeled with x (x = 0, 1, 2). If there is a total mean cordial labeling on a graph G, then we will call G is Total Mean Cordial. Here, We investigate the Total Mean Cordial labeling behaviour of prism, gear, helms.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.3236-3238
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• 1999

This paper proposes a new face recognition technique in the dynamic link architecture which shows robustness against size variation and distortion. The face recognition technique in the dynamic link architecture so far was not appropriate for the recognition of various size of faces because of the fixed size of the graph and the fixed value of a of the Gabor filter not considering the size of the face. The proposed face recognition algorithm can represent the input facial image by a suitable size of labeled graph, and it can also adjust the dilation width and the height of the vibrating amplitude of the Gabor filter, thus face recognition in the dynamic link architecture is even applicable regardless of the size of the face.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.731-734
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• 2000

We present a novel graph labeling problem called Fibonacci edge labeling. The constraint in this labeling is placed on the allowable edge label which is the difference between the labels of endvertices of an edge. Each edge label should be (3m+2)-th Fibonacci numbers. We show that every Fibonacci tree can be labeled Fibonacci edge labeling. The labelings on the Fibonacci trees are applied to their embeddings into Fibonacci Circulants.

• Journal of the Korean Data and Information Science Society
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• 제21권3호
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• pp.461-470
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• 2010

라벨 있는 자료가 분류규칙을 만들 만큼 충분하지 않거나, 라벨 없는 자료가 분류규칙을 만드는데 도움을 줄 수 있는 경우에는 라벨 있는 자료와 라벨 없는 자료를 모두 사용하는 준지도분류가 더 효과적이다. 준지도분류 중 그래프기반 다양체정칙법이 개발되어 최근에 많은 연구가 이루어지고 있다. 본 연구에서는 통계적학습에서 좋은 성능을 보이는 최소제곱 서포터벡터기계를 준지도분류에 적용시키는 방법을 제안한다. 모의실험을 통해 제안된 방법이 라벨 없는 자료를 잘 활용하는 것을 볼 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2607-2627
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• 2017

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.