• 응용통계연구
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• 제35권2호
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• pp.285-298
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• 2022

연속 수축 사전분포는 spike and slab 사전분포와 더불어, 희박 회귀계수 벡터 또는 공분산 행렬에 대한 베이지안 추론을 위해 널리 사용되고 있다. 특히 고차원 상황에서, 연속 수축 사전분포는 spike and slab 사전분포에 비해 매우 작은 모수공간을 가짐으로써 계산적인 이점을 가진다. 하지만 연속 수축 사전분포는 정확히 0인 값을 생성하지 않기 때문에, 이를 이용한 변수 선택이 자연스럽지 않다는 문제가 있다. 비록 연속 수축 사전분포에 기반한 변수 선택 방법들이 개발되어 있기는 하지만, 이들에 대한 포괄적인 비교연구는 거의 진행되어 있지 않다. 본 논문에서는, 연속 수축 사전분포에 기반한 두 가지의 변수 선택 방법들을 비교하려 한다. 첫 번째 방법은 신용구간에 기반한 변수 선택, 두 번째 방법은 최근 Li와 Pati (2017)가 개발한 sequential 2-means 알고리듬이다. 두 방법에 대한 간략한 소개를 한 뒤, 다양한 모의실험 상황에서 자료를 생성하여 두 방법들의 성능을 비교하였다. 끝으로, 모의실험으로부터 발견한 몇 가지 사실들을 기술하고, 이로부터 몇 가지 제안을 하며 논문을 마치려 한다.

• ETRI Journal
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• 제45권3호
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• pp.492-504
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• 2023

Researchers have recently shown an increased interest in estimating the direction-of-arrival (DOA) of wideband noncircular sources, but existing studies have been restricted to subspace-based methods. An off-grid sparse recovery-based algorithm is proposed in this paper to improve the accuracy of existing algorithms in low signal-to-noise ratio situations. The covariance and pseudo covariance matrices can be jointly represented subject to block sparsity constraints by taking advantage of the joint sparsity between signal components and bias. Furthermore, the estimation problem is transformed into a single measurement vector problem utilizing the focused operation, resulting in a significant reduction in computational complexity. The proposed algorithm's error threshold and the Cramer-Rao bound for wideband noncircular DOA estimation are deduced in detail. The proposed algorithm's effectiveness and feasibility are demonstrated by simulation results.

• 대한원격탐사학회지
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• 제1권1호
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.