• Communications for Statistical Applications and Methods
• /
• 제10권2호
• /
• pp.387-397
• /
• 2003

Generally, most of researches that need a variable-clustering process use an exploratory factor analysis technique or a divisive hierarchical variable-clustering method based on a correlation matrix. And some researchers apply a object-clustering method to a distance matrix transformed from a correlation matrix, though this approach is known to be improper. On this paper an agglomerative hierarchical variable-clustering method based on a correlation matrix itself is suggested. It is derived from a geometric concept by using variate-spaces and a characterizing variate.

• Journal of the Korean Data and Information Science Society
• /
• 제22권4호
• /
• pp.727-733
• /
• 2011

주식수익률간의 상관행렬 분석을 통해 유의미한 정보를 추출 활용하는 것은 주식시장을 이해하는데 매우 중요하다. 최근 확률행렬이론을 이용 상관행렬을 분석하는 연구들이 많이 진행되어 왔는데, 본 논문에서는 단일 요인 모형을 확률행렬이론에 적용 한국주식시장에서 주식수익률간의 상관행렬에 관한 유의미한 정보를 추출하였다. 특히 단일 요인을 도입 상관행렬을 분석한 결과가 실제 데이터를 잘 설명함을 관찰하였고, 단일 요인 모형의 유용성을 확인하였다.

• Journal of the Korean Statistical Society
• /
• 제22권2호
• /
• pp.339-345
• /
• 1993

It is well known that distribution of functions of eigen values and vectors of a certain matrix plays an important role in multivariate analysis. This paper deals with the transformation of a correlation-type random matrix to its eigen values and vectors. Properties of the transformation are also considered. The results obtained are applied to express the joint distribution of eigen values and vectors of the correlation matrix when sample is taken from a m-variate spherical distribution.

• 한국항행학회논문지
• /
• 제26권3호
• /
• pp.166-171
• /
• 2022

본 논문에서는 선형 배열 균일 안테나에 입사하는 신호들의 상관행렬을 강건하게 생성하여 부공간 기반 기법의 도래각 추정 성능을 향상시키는 알고리즘을 제안한다. 기존의 부공간 기반 도래각 추정 기법은 상관행렬을 구한 후 신호 부공간과 잡음 부공간으로 분리하여 도래각을 추정한다. 그러나, 낮은 SNR, 작은 개수의 스냅샷에서 구해지는 상관행렬의 성분은 안테나의 잡음 성분으로 인하여 신호 부공간을 부정확하게 추정하여 도래각 추정 성능을 저하시킨다. 따라서, 기존의 상관행렬로부터 구해지는 가상의 신호 벡터를 슬라이딩 방식으로 배열함으로써 강건한 상관행렬을 생성한다. 기존의 상관행렬과 제안하는 강건한 상관행렬의 비교 분석을 위하여, 부공간 기반 기법의 대표적 방법인 MUSIC, ESPRIT을 이용하였다. 시뮬레이션 결과, 계산 복잡도는 기존의 상관행렬 대비 2.5% 이내 증가하였으나, 도래각 추정성능은 RMSE 1° 기준 SNR이 MUSIC, ESPRIT 모두 3dB 이상의 우수한 도래각 추정 성능을 보였다.

• 대한전자공학회논문지TC
• /
• 제43권10호
• /
• pp.18-26
• /
• 2006

목표신호와 간섭신호 사이에 상호 상관성분이 크게 존재할 경우에도 성능저하를 극복할 수 있는 새로운 빔형성 알고리듬을 제안한다. 제안하는 방식을 이용하여 상관행렬로부터 간섭신호의 도달각을 바로 구하고 입력신호의 상관행렬 내에 존재하는 상호상관 성분을 제거할 수 있음을 보였다. 제안한 방식을 적용하였을 경우에는 공간평균(Spatial Averaging)을 사용하는 것에 비해서 간섭신호의 크기를 줄이는 데 있어서 성능향상이 있음을 모의실험 결과를 통해서 보였다.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
• /
• pp.208-211
• /
• 2004

Landmark matching is one of an important algorithm for navigation of satellite images. This paper proposes a fast landmark matching algorithm using a MGLI (Moving Guide-Line Image). For searching the matched point between the landmark chip and a part of image, correlation matrix is used generally, but the full-sized correlation matrix has a drawback requiring plenty of time for matching point calculation. MGLI includes thick lines for fast calculation of correlation matrix. In the MGLI, width of the thick lines should be determined by satellite position changes and navigation error range. For the fast landmark matching, the MGLI provides guided line for a landmark chip we want to match, so that the proposed method should reduce candidate areas for correlation matrix calculation. This paper will show how much time is reduced in the proposed fast landmark matching algorithm compared to general ones.

• Journal of applied mathematics & informatics
• /
• 제30권1_2호
• /
• pp.201-210
• /
• 2012

A modified multiplicative update algorithms is presented for computing the nearest correlation matrix. The convergence property is analyzed in details and a sufficient condition is given to guarantee that the proposed approach will not breakdown. A number of numerical experiments show that the modified multiplicative updating algorithm is efficient, and comparable with the existing algorithms.

• 전자공학회논문지B
• /
• 제31B권5호
• /
• pp.134-140
• /
• 1994

In this paper, an algorithm of CBAM(Combination Bidirectional Associative Memory) model proposes, analyzes and tests CBAM model `s performancess by simulating with recalls and recognitions of patterns. In learning-procedure each correlation matrix of training patterns is obtained. As each correlation matrix's some elements correspond to juxtaposition, all correlation matrices are merged into one matrix (Combination Correlation Matrix, CCM). In recall-procedure, CCM is decomposed into a number of correlation matrices by spiliting its elements into the number of elements corresponding to all training patterns. Recalled patterns are obtained by multiplying input pattern with all correlation matrices and selecting a pattern which has the smallest value of energy function. By using a CBAM model, we have some advantages. First, all pattern having less than 20% of noise can be recalled. Second, memory capacity of CBAM model, can be further increased to include English alphabets or patterns. Third, learning time of CBAM model can be reduced greatly because of operation to make CCM.

• Communications for Statistical Applications and Methods
• /
• 제29권6호
• /
• pp.655-664
• /
• 2022

On the motivation by an integrative study of multi-omics data, we are interested in estimating the structure of the sparse cross correlation matrix of two high-dimensional random vectors. We rewrite the problem as a multiple testing problem and propose a new method to estimate the sparse structure of the cross correlation matrix. To do so, we test the correlation coefficients simultaneously and threshold the correlation coefficients by controlling FRD at a predetermined level α. Further, we apply the proposed method and an alternative adaptive thresholding procedure by Cai and Liu (2016) to the integrative analysis of the protein expression data (X) and the mRNA expression data (Y) in TCGA breast cancer cohort. By varying the FDR level α, we show that the new procedure is consistently more efficient in estimating the sparse structure of cross correlation matrix than the alternative one.

• Communications for Statistical Applications and Methods
• /
• 제22권6호
• /
• pp.665-674
• /
• 2015

Lack of a general matrix formula hampers implementation of the semi-partial correlation, also known as part correlation, to the higher-order coefficient. This is because the higher-order semi-partial correlation calculation using a recursive formula requires an enormous number of recursive calculations to obtain the correlation coefficients. To resolve this difficulty, we derive a general matrix formula of the semi-partial correlation for fast computation. The semi-partial correlations are then implemented on an R package ppcor along with the partial correlation. Owing to the general matrix formulas, users can readily calculate the coefficients of both partial and semi-partial correlations without computational burden. The package ppcor further provides users with the level of the statistical significance with its test statistic.