• 제목/요약/키워드: dimension reduction method

검색결과 251건 처리시간 0.019초

Comparison of accuracy between LC model and 4-PFM when COVID-19 impacts mortality structure

  • Choi, Janghoon
    • Communications for Statistical Applications and Methods
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    • 제28권3호
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    • pp.233-250
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    • 2021
  • This paper studies if the accuracies of mortality models (LC model vs. 4-parametric model) are aggravated if a mortality structure changes due to the impact of COVID-19. LC model (LCM) uses dimension reduction for fitting to the log mortality matrix so that the performance of the dimension reduction method may not be good when the matrix structure changes. On the other hand, 4-parametric factor model (4-PFM) is designed to use factors for fitting to log mortality data by age groups so that it would be less affected by the change of the mortality structure. In fact, the forecast accuracies of LCM are better than those of 4-PFM when life-tables are used whereas those of 4-PFM are better when the mortality structure changes. Thus this result shows that 4-PFM is more reliable in performance to the structural changes of the mortality. To support the accuracy changes of LCM the functional aspect is explained by computing eigenvalues produced by singular vector decomposition

The use of support vector machines in semi-supervised classification

  • Bae, Hyunjoo;Kim, Hyungwoo;Shin, Seung Jun
    • Communications for Statistical Applications and Methods
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    • 제29권2호
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    • pp.193-202
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    • 2022
  • Semi-supervised learning has gained significant attention in recent applications. In this article, we provide a selective overview of popular semi-supervised methods and then propose a simple but effective algorithm for semi-supervised classification using support vector machines (SVM), one of the most popular binary classifiers in a machine learning community. The idea is simple as follows. First, we apply the dimension reduction to the unlabeled observations and cluster them to assign labels on the reduced space. SVM is then employed to the combined set of labeled and unlabeled observations to construct a classification rule. The use of SVM enables us to extend it to the nonlinear counterpart via kernel trick. Our numerical experiments under various scenarios demonstrate that the proposed method is promising in semi-supervised classification.

PCA-SVM 기법을 이용한 차량의 색상 인식 (PCA-SVM Based Vehicle Color Recognition)

  • 박선미;김구진
    • 정보처리학회논문지B
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    • 제15B권4호
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    • pp.285-292
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    • 2008
  • 색상 히스토그램은 영상의 색상 특징을 표현하기 위한 특징 벡터로 빈번히 사용되지만, 고차원의 특징 벡터를 생성하므로 효율성의 면에서 한계점을 갖고 있다. 본 논문에서는 주어진 차량 영상의 색상 히스토그램에 PCA (principal components analysis) 기법을 적용하여 특징 벡터의 차원을 축소시키는 방법을 제안한다. 차원이 축소된 특징 벡터들에 대해서는 SVM (support vector machine) 기법을 적용하여 차량 색상을 인식하기 위해 사용한다. 특징 벡터의 차원을 1/32로 축소한 결과, 차원이 축소되기 이전의 특징 벡터와 비교하여 약 1.42%의 미소한 차이로 색상 인식 성공률이 감소하였다. 또한, 색상 인식의 수행 시간은 1/31로 단축됨으로써 효율적으로 색상 인식을 수행할 수 있었다.

지도학습 기반의 차원축소 모델을 이용한 특허 빅데이터 예측에 관한 연구 (A Study on prediction of patent big data using supervised learning with dimension reduction model)

  • 이주현;이준석;강지호;박상성;장동식;홍성욱;김선영
    • 디지털산업정보학회논문지
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    • 제15권4호
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    • pp.41-49
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    • 2019
  • Patents are system to promote the development of industry by disclosing technology. The importance of recent patent is being emphasized. For this reason, companies apply for many patents. And they analyze the patent. Patent analysis helps to protect and foster their technology. Previously this method has been carried out by experts. Expert-based patent analysis, however, has the disadvantage of being time-consuming and expensive. Consequently, we try to solve this problems by developing prediction model. Therefore, this paper proposes a data-based patent analysis method using quantitative indicator and textual information. We confirmed the practical applicability of the proposed method through 1,831 autonomous vehicle patents. As a result, it was possible to confirmed that safety and lane detection related technologies are important.

Simultaneous Determination of Polycyclic Aromatic Hydrocarbons and Their Nitro-derivatives in Airborne Particulates by Using Two-dimensional High-performance Liquid Chromatography with On-line Reduction and Fluorescence Detection

  • Boongla, Yaowatat;Orakij, Walaiporn;Nagaoka, Yuuki;Tang, Ning;Hayakawa, Kazuichi;Toriba, Akira
    • Asian Journal of Atmospheric Environment
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    • 제11권4호
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    • pp.283-299
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    • 2017
  • An analytical method using high-performance liquid chromatography (HPLC) with fluorescence (FL) detection was developed for simultaneously analyzing 10 polycyclic aromatic hydrocarbons (PAHs) and 18 nitro-derivatives of PAHs (NPAHs). The two-dimensional HPLC system consists of an on-line clean-up and reduction for NPAHs in the 1st dimension, and separation of the PAHs and the reduced NPAHs and their FL detection in the 2nd dimension after column-switching. To identify an ideal clean-up column for removing sample matrix that may interfere with detection of the analytes, the characteristics of 8 reversed-phase columns were evaluated. The nitrophenylethyl (NPE)-bonded silica column was selected because of its shorter elution band and larger retention factors of the analytes due to strong dipole-dipole interactions. The amino-substituted PAHs (reduced NPAHs), PAHs and deuterated internal standards were separated on polymeric octadecyl-bonded silica (ODS) columns and by dual-channel detection within 120 min including clean-up and reduction steps. The limits of detection were 0.1-9.2 pg per injection for PAHs and 0.1-140 pg per injection for NPAHs. For validation, the method was applied to analyze crude extracts of fine particulate matter ($PM_{2.5}$) samples and achieved good analytical precision and accuracy. Moreover, the standard reference material (SRM1649b, urban dust) was analyzed by this method and the observed concentrations of PAHs and NPAHs were similar to those in previous reports. Thus, the method developed here-in has the potential to become a standard HPLC-based method, especially for NPAHs.

유전자 알고리즘을 이용한 직선 방음벽의 최적 설계 (Optimal Design of Straight Noise Barriers Using Genetic Algorithm)

  • 하지형;최태묵;조대승
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 2001년도 추계학술대회논문집 I
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    • pp.127-132
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    • 2001
  • A successful design approach for noise barriers should be multidisciplinary because noise reduction goals influence both acoustical and non-acoustical considerations, such as maintenance, safety, physical construction, cost, and visual impact. These various barrier design options are closely related with barrier dimensions. In this study, we have proposed an optimal design method of straight noise barriers using genetic algorithm, providing a barrier having the smallest dimension and achieving the specified noise reduction at a receiver region exposed to the industry and traffic noise, to help a successful barrier design.

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NUMERICAL SOLUTION OF EQUILIBRIUM EQUATIONS

  • Jang, Ho-Jong
    • 대한수학회논문집
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    • 제15권1호
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    • pp.133-142
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    • 2000
  • We consider some numerical solution methods for equilibrium equations Af + E$^{T}$ λ = r, Ef = s. Algebraic problems of this form evolve from many applications such as structural optimization, fluid flow, and circuits. An important approach, called the force method, to the solution to such problems involves dimension reduction nullspace computation for E. The purpose of this paper is to investigate the substructuring method for the solution step of the force method in the context of the incompressible fluid flow. We also suggests some iterative methods based upon substructuring scheme..

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A Bayesian Model-based Clustering with Dissimilarities

  • Oh, Man-Suk;Raftery, Adrian
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2003년도 추계 학술발표회 논문집
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    • pp.9-14
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    • 2003
  • A Bayesian model-based clustering method is proposed for clustering objects on the basis of dissimilarites. This combines two basic ideas. The first is that tile objects have latent positions in a Euclidean space, and that the observed dissimilarities are measurements of the Euclidean distances with error. The second idea is that the latent positions are generated from a mixture of multivariate normal distributions, each one corresponding to a cluster. We estimate the resulting model in a Bayesian way using Markov chain Monte Carlo. The method carries out multidimensional scaling and model-based clustering simultaneously, and yields good object configurations and good clustering results with reasonable measures of clustering uncertainties. In the examples we studied, the clustering results based on low-dimensional configurations were almost as good as those based on high-dimensional ones. Thus tile method can be used as a tool for dimension reduction when clustering high-dimensional objects, which may be useful especially for visual inspection of clusters. We also propose a Bayesian criterion for choosing the dimension of the object configuration and the number of clusters simultaneously. This is easy to compute and works reasonably well in simulations and real examples.

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이차원 퓨리에 변환의 크기와 위상을 이용한 커버곡 검색 (Cover song search based on magnitude and phase of the 2D Fourier transform)

  • 서진수
    • 한국음향학회지
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    • 제37권6호
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    • pp.518-524
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    • 2018
  • 라이브 음악 또는 리메이크를 통해서 재발매된 음악을 원곡의 커버곡이라 부른다. 본 논문은 고속 커버곡 검색을 위한 특징 축약을 위해 2차원 퓨리에 변환을 이용하는 방법을 연구하였다. 이차원 퓨리에 변환은 조변화에 대해서 불변성을 가지고 있으므로, 커버곡 검색을 위한 특징 축약 방법으로 적합하다. 기존 퓨리에 변환 방법에서는 크기값 만을 활용하였으나, 본 논문에서는 인접한 크로마 블록은 같은 조변화를 가진다는 가정하에 위상 정보를 추가로 활용하는 방법을 제안하였다. 두 가지 커버곡 실험 데이터셋에서 성능 비교를 수행하였으며, 제안된 방법이 기존 방법에 비해서 우수한 커버곡 검색 정확도를 보임을 확인하였다.

대규모 지하 광산 구조물의 규모 결정을 위한 수치해석적 설계 접근 (Numerical Design Approach to Determining the Dimension of Large-Scale Underground Mine Structures)

  • 이윤수;박도현;선우춘;김교원;강중석
    • 터널과지하공간
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    • 제22권2호
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    • pp.120-129
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    • 2012
  • 최근 친환경적 광산 개발에 대한 사회적 요구에 따라 갱외 시설물도 갱내화하는 경향이 있다. 지하 광산 구조물은 보통 높이보다 폭이 큰 공간을 필요로 하기 때문에 안정성 평가가 중요하다. 본 연구에서는 강도감소법을 이용하여 안전율을 분석하고, 강도감소법과 다변량 회귀분석을 조합하여 지하 광산 구조물의 규모 결정을 위한 수치해석적 설계의 접근방법을 수행하였다. 설계 매개변수는 암반의 전단강도와 측압계수 그리고 지하 광산 구조물의 폭과 설치심도이다. 지하 광산 구조물의 안정성은 입력된 매개변수의 서로 다른 조건하에서 강도 감소법으로 계산된 안전율의 개념으로 평가되었으며, 다양한 다변량 회귀분석을 통해 안전율에 대한 적합한 함수를 얻었다. 최종적으로 최적의 회귀모델을 사용하여 지하 광산 구조물의 규모 결정에 있어서의 초기 설계 정보를 제공하는 도표를 제안했다.