• 대한수학회논문집
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• 제15권4호
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• pp.587-595
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• 2000

We contain a simple construction for the sparse n x n connected orthogonal matrices which have a row of p nonzero entries with 2$\leq$p$\leq$n. Moreover, we study the analogous sparsity problem for an m x n connected row-orthogonal matrices.

• Korean Journal of Mathematics
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• 제7권1호
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• pp.61-69
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• 1999

We determine sparse orthogonal matrices of order $n$ which is fully indecomposable by weaving.

• 대한수학회지
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• 제36권2호
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• pp.333-344
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• 1999

In [4], it was shown that an n by n orthogonal matrix which has a row of nonzeros has at least ( log2n + 3)n - log2n +1 nonzero entries. In this paper, the matrices achieving these bounds are constructed. The analogous sparsity problem for m by n row-orthogonal matrices which have a row of nonzeros in conjectured.

• 대한수학회보
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• 제36권1호
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• pp.119-129
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• 1999

In [1], it was shown that for $n\geq 2$ the least number of nonzero entries in an $n\times n$ orthogonal matrix is not direct summable is 4n-4, and zero patterns of the $n\times n$ orthogonal matrices with exactly 4n-4 nonzero entries were determined. In this paper, we construct $n\times n$ orthogonal matrices with exactly 4n-r nonzero entries. furthermore, we determine m${\times}$n sparse row-orthogonal matrices.

• 대한전자공학회논문지SP
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• 제49권2호
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• pp.122-129
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• 2012

Compressive sensing 분야에서 orthogonal matching pursuit (OMP) 알고리듬은 underdetermined 시스템의 스파스 (sparse) 신호를 복구하는 대표적인 greedy 알고리듬으로 많은 관심을 받고 있다. 본 논문에서는 OMP 알고리듬의 반복과정에서 하나 이상의 support들을 선택할 수 있도록 하는 OMP 알고리듬의 일반화된 형태의 generalized orthogonal matching pursuit (gOMP)기법을 제안한다. gOMP가 완벽한 신호 복원을 보장하기 위해 restricted isometry property (RIP)를 이용한 충분조건, ${\delta}_{NK}$ < $\frac{\sqrt{N}}{\sqrt{K}+2\sqrt{N}}$을 제시한다. 실험을 통해 gOMP는 매 반복과정에서 하나 이상의 support들를 선택함으로써 높은 복원 성능과 낮은 복잡도를 가짐을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.281-282
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• 2007

We address a new representation of DCT/DFT/Wavelet matrices via one hybrid architecture. Based on an element inverse matrix factorization algorithm, we show that the OCT, OFT and Wavelet which based on Haar matrix have the similarrecursive computational pattern, all of them can be decomposed to one orthogonal character matrix and a special sparse matrix. The special sparse matrix belongs to Jacket matrix, whose inverse can be from element-wise inverse or block-wise inverse. Based on this trait, we can develop a hybrid architecture.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제4권1호
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• pp.109-119
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• 2000

It was shown that if Q is a fully indecomposable $n{\times}n$ orthogonal matrix then Q has at least 4n-4 nonzero entries in 1993. In this paper, we show that for each integer p with $4n-4{\leq}p{\leq}n^2$, there exist a fully indecomposable $n{\times}n$ orthogonal matrix with exactly p nonzero entries. Furthermore, we obtain a method of construction of a fully indecomposable $n{\times}n$ orthogonal matrix which has exactly 4n-4 nonzero entries. This is a part of the study in sparse matrices.

• Journal of Electrical Engineering and information Science
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• 제1권2호
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• pp.125-133
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• 1996

In this paper, we dicuss on the properties related to the circular filtering in orthogonal transform domain. The efficient filtering schemes in six orthogonal transform domains are presented by generalizing the convolution-multiplication property of the DFT. In brief, the circular filtering can be accomplished by multiplying the transform domain filtering matrix W, which is shown to be very sparse, yielding the computational gains compared with the time domain processing. As an application, decimation and interpolation techniques in orthogonal transform domains are also investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2468-2483
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• 2017

The construction of completely random sensing matrices of Compressive Sensing requires a large number of random numbers while that of deterministic sensing operators often needs complex mathematical operations. Thus both of them have difficulty in acquiring large signals efficiently. This paper focuses on the enhancement of the practicability of the structurally random matrices and proposes a semi-deterministic sensing matrix called Partial Kronecker product of Identity and Hadamard (PKIH) matrix. The proposed matrix can be viewed as a sub matrix of a well-structured, sparse, and orthogonal matrix. Only the row index is selected at random and the positions of the entries of each row are determined by a deterministic sequence. Therefore, the PKIH significantly decreases the requirement of random numbers, which has a complex generating algorithm, in matrix construction and further reduces the complexity of sampling. Besides, in order to process large signals, the corresponding fast sampling algorithm is developed, which can be easily parallelized and realized in hardware. Simulation results illustrate that the proposed sensing matrix maintains almost the same performance but with at least 50% less random numbers comparing with the popular sampling matrices. Meanwhile, it saved roughly 15%-35% processing time in comparison to that of the SRM matrices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.5015-5038
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• 2018

In this paper, a novel spectral-spatial joint sparse representation algorithm for hyperspectral image classification is proposed based on multi-layer superpixels in various scales. Superpixels of various scales can provide complete yet redundant correlated information of the class attribute for test pixels. Therefore, we design a joint sparse model for a test pixel by sampling similar pixels from its corresponding superpixels combinations. Firstly, multi-layer superpixels are extracted on the false color image of the HSI data by principal components analysis model. Secondly, a group of discriminative sampling pixels are exploited as reconstruction matrix of test pixel which can be jointly represented by the structured dictionary and recovered sparse coefficients. Thirdly, the orthogonal matching pursuit strategy is employed for estimating sparse vector for the test pixel. In each iteration, the approximation can be computed from the dictionary and corresponding sparse vector. Finally, the class label of test pixel can be directly determined with minimum reconstruction error between the reconstruction matrix and its approximation. The advantages of this algorithm lie in the development of complete neighborhood and homogeneous pixels to share a common sparsity pattern, and it is able to achieve more flexible joint sparse coding of spectral-spatial information. Experimental results on three real hyperspectral datasets show that the proposed joint sparse model can achieve better performance than a series of excellent sparse classification methods and superpixels-based classification methods.