• Journal of the Korean Institute of Telematics and Electronics
• /
• v.11 no.2
• /
• pp.1-4
• /
• 1974

Matrix inversion is very inefficient for computing direct solutions of the large sparse systems of linear equations that arise in many network problems. This paper describes some computer programming techniques for taking advantage of the sparsity of the admittance matrix. with this method, direct solutions are computed from sparse matrix. It is Possible to gain a significant reduction in computing time, memory and round-off emir[r. Retails of the method, numerical examples and programming are given.

• Proceedings of the Korea Contents Association Conference
• /
• 2008.05a
• /
• pp.92-95
• /
• 2008

NMF(Non-negative Matrix Factorization) has been proposed as an useful algorithm for feature extraction. Using NMF, we can extract low-dimensional feature vectors. Also, we can find clustering effects in the NMF algorithm. Also, it is reported that the sparse NMF algorithm shows better clustering effects. This paper compares the two approaches in the viewpoint of clustering effects.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.5 no.1
• /
• pp.60-69
• /
• 1980

The computerized analysis of complicated circuits requires large memory capacity and considerable length of time. In order to enhance the efficiency of memory capacity and the executing time, Sparse Matrix is applied to the solution of simultaneous equations required for the analysis of amplification circuit. The optimization Subroutine, FMFP is utilized for the decision of optimum element parameters of an equalizer amplifier.

• Korean Journal of Mathematics
• /
• v.7 no.1
• /
• pp.61-69
• /
• 1999

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

• Communications for Statistical Applications and Methods
• /
• v.29 no.6
• /
• 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.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.13 no.1
• /
• pp.617-627
• /
• 2021

Underwater Acoustic Channels (UAC) have inherent sparse characteristics. The traditional adaptive equalization techniques do not utilize this feature to improve the performance. In this paper we consider the Variable Adaptive Subgradient Projection (V-ASPM) method to derive a new sparse equalization algorithm based on the Minimum Symbol Error Rate (MSER) criterion. Compared with the original MSER algorithm, our proposed scheme adds sparse matrix to the iterative formula, which can assign independent step-sizes to the equalizer taps. How to obtain such proper sparse matrix is also analyzed. On this basis, the selection scheme of the sparse matrix is obtained by combining the variable step-sizes and equalizer sparsity measure. We call the new algorithm Sparse-Control Proportional-MSER (SC-PMSER) equalizer. Finally, the proposed SC-PMSER equalizer is embedded into a turbo receiver, which perform turbo decoding, Digital Phase-Locked Loop (DPLL), time-reversal receiving and multi-reception diversity. Simulation and real-field experimental results show that the proposed algorithm has better performance in convergence speed and Bit Error Rate (BER).

• ETRI Journal
• /
• v.36 no.1
• /
• pp.167-170
• /
• 2014

In this letter, we propose an unsupervised framework for speech noise reduction based on the recent development of low-rank and sparse matrix decomposition. The proposed framework directly separates the speech signal from noisy speech by decomposing the noisy speech spectrogram into three submatrices: the noise structure matrix, the clean speech structure matrix, and the residual noise matrix. Evaluations on the Noisex-92 dataset show that the proposed method achieves a signal-to-distortion ratio approximately 2.48 dB and 3.23 dB higher than that of the robust principal component analysis method and the non-negative matrix factorization method, respectively, when the input SNR is -5 dB.

• ETRI Journal
• /
• v.39 no.1
• /
• pp.21-29
• /
• 2017

This paper addresses the problem of unsupervised speech separation based on robust non-negative matrix factorization (RNMF) with ${\beta}$-divergence, when neither speech nor noise training data is available beforehand. We propose a robust version of non-negative matrix factorization, inspired by the recently developed sparse and low-rank decomposition, in which the data matrix is decomposed into the sum of a low-rank matrix and a sparse matrix. Efficient multiplicative update rules to minimize the ${\beta}$-divergence-based cost function are derived. A convolutional extension of the proposed algorithm is also proposed, which considers the time dependency of the non-negative noise bases. Experimental speech separation results show that the proposed convolutional RNMF successfully separates the repeating time-varying spectral structures from the magnitude spectrum of the mixture, and does so without any prior training.

• Journal of Digital Contents Society
• /
• v.16 no.4
• /
• pp.605-613
• /
• 2015

This paper addresses the problem to make sparse the projection matrix in pattern recognition method. Recently, the size of computer program is often restricted in embedded systems. It is very often that developed programs include some constant data. For example, many pattern recognition programs use the projection matrix for dimension reduction. To improve the recognition performance, very high dimensional feature vectors are often extracted. In this case, the projection matrix can be very big. Recently, RSR(roated sparse regression) method[1] was proposed. This method has been proved one of the best algorithm that obtains the sparse matrix. We propose three methods to improve the RSR; outlier removal, sampling and elastic net RSR(E-RSR) in which the penalty term in RSR optimization function is replaced by that of the elastic net regression. The experimental results show that the proposed methods are very effective and improve the sparsity rate dramatically without sacrificing the recognition rate compared to the original RSR method.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.7
• /
• pp.99-106
• /
• 1995

Frequency response functions are of great use in dynamic analysis of structural systems. The present paper proposes an efficient method for computation of the frequency rewponse functions of linear structural dynamic models with a sparse, non-proportional damping matrix. An exact condensation procedure is proposed which enables the present method to condense the matrices without resulting in any errors. Also, an iterative scheme is proposed to be able to avoid matrix inversion in computing frequency response matrix. The proposed method is illustrated through a numerical example.