• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4556-4572
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• 2015

Dynamic texture (DT) recognition is a challenging problem in numerous applications. In this study, we propose a new algorithm for DT recognition based on group sparsity structure in conjunction with chaotic feature vector. Bag-of-words model is used to represent each video as a histogram of the chaotic feature vector, which is proposed to capture self-similarity property of the pixel intensity series. The recognition problem is then cast to a group sparsity model, which can be efficiently optimized through alternating direction method of multiplier algorithm. Experimental results show that the proposed method exhibited the best performance among several well-known DT modeling techniques.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.173-180
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• 2014

Compressive sensing is a new signal acquisition paradigm that enables sparse/compressible signal to be sampled under the Nyquist-rate. To fully benefit from its much simplified acquisition process, huge efforts have been made on improving the performance of compressive sensing recovery. However, concerning color images, compressive sensing recovery lacks in addressing image characteristics like energy distribution or human visual system. In order to overcome the problem, this paper proposes a new group-sparsity hard thresholding process by preserving some RGB-grouped coefficients important in both terms of energy and perceptual sensitivity. Moreover, a smoothed group-sparsity iterative hard thresholding algorithm for compressive sensing of color images is proposed by incorporating a frame-based filter with group-sparsity hard thresholding process. In this way, our proposed method not only pursues sparsity of image in transform domain but also pursues smoothness of image in spatial domain. Experimental results show average PSNR gains up to 2.7dB over the state-of-the-art group-sparsity smoothed recovery method.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.9
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• pp.106-113
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• 2015

This paper integrates human visual system (HVS) characteristics into compressed sensing recovery of color images. The proposed visual weighting of each color channel in group-sparsity minimization not only pursues sparsity level of image but also reflects HVS characteristics well. Additionally, an edge-preserving filter is embedded in the scheme to remove noise while preserving edges of image so that quality of reconstructed image is further enhanced. Experimental results show that the average PSNR of the proposed method is 0.56 ~ 4dB higher than that of the state-of-the art group-sparsity minimization method. These results prove the excellence of the proposed method in both terms of objective and subjective qualities.

• Genomics & Informatics
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• v.20 no.4
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• pp.48.1-48.7
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• 2022

Penalized regression has been widely used in genome-wide association studies for joint analyses to find genetic associations. Among penalized regression models, the least absolute shrinkage and selection operator (Lasso) method effectively removes some coefficients from the model by shrinking them to zero. To handle group structures, such as genes and pathways, several modified Lasso penalties have been proposed, including group Lasso and sparse group Lasso. Group Lasso ensures sparsity at the level of pre-defined groups, eliminating unimportant groups. Sparse group Lasso performs group selection as in group Lasso, but also performs individual selection as in Lasso. While these sparse methods are useful in high-dimensional genetic studies, interpreting the results with many groups and coefficients is not straightforward. Lasso's results are often expressed as trace plots of regression coefficients. However, few studies have explored the systematic visualization of group information. In this study, we propose a multi-level polar Lasso (MP-Lasso) chart, which can effectively represent the results from group Lasso and sparse group Lasso analyses. An R package to draw MP-Lasso charts was developed. Through a real-world genetic data application, we demonstrated that our MP-Lasso chart package effectively visualizes the results of Lasso, group Lasso, and sparse group Lasso.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.583-599
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• 2015

We apply the Bayesian matching pursuit (BMP) algorithm to the estimation of time-frequency selective channels in orthogonal frequency division multiplexing (OFDM) systems. By exploiting prior statistics and sparse characteristics of propagation channels, the Bayesian method provides a more accurate and efficient detection of the channel status information (CSI) than do conventional sparse channel estimation methods that are based on compressive sensing (CS) technologies. Using a reasonable approximation of the system model and a skillfully designed pilot arrangement, the proposed estimation scheme is able to address the Doppler-induced inter-carrier interference (ICI) with a relatively low complexity. Moreover, to further reduce the computational cost of the channel estimation, we make some modifications to the BMP algorithm. The modified algorithm can make good use of the group-sparse structure of doubly selective channels and thus reconstruct the CSI more efficiently than does the original BMP algorithm, which treats the sparse signals in the conventional manner and ignores the specific structure of their sparsity patterns. Numerical results demonstrate that the proposed Bayesian estimation has a good performance over rapidly time-varying channels.

• Journal of Information Technology Applications and Management
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• v.20 no.3_spc
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• pp.219-230
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• 2013

Real time accessiblity and agility in Ubiquitous-commerce is required under ubiquitous computing environment. The Research has been actively processed in e-commerce so as to improve the accuracy of recommendation. Existing Collaborative filtering (CF) can not reflect contents of the items and has the problem of the process of selection in the neighborhood user group and the problems of sparsity and scalability as well. Although a system has been practically used to improve these defects, it still does not reflect attributes of the item. In this paper, to solve this problem, We can use a implicit method which is used by customer's data and purchase history data. We propose a new clustering method of weighted preference for customer using k-means clustering and Bayesian network in order to improve the accuracy of recommendation. To verify improved performance of the proposed system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2748-2766
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• 2016

Compared with traditional patch-based sparse representation, recent studies have concluded that group-based sparse representation (GSR) can simultaneously enforce the intrinsic local sparsity and nonlocal self-similarity of images within a unified framework. This article investigates an accelerated split Bregman method (SBM) that is based on GSR which exploits image compressive sensing (CS). The computational efficiency of accelerated SBM for the measurement matrix of a partial Fourier matrix can be further improved by the introduction of a fast Fourier transform (FFT) to derive the enhanced algorithm. In addition, we provide convergence analysis for the proposed method. Experimental results demonstrate that accelerated SBM is potentially faster than some existing image CS reconstruction methods.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.31-38
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• 1988

This paper presents a methodology for the development of a part classification and coding system suited to each individual company. When coding a group of parts for a specific company by a general purpose part classification & coding system like OPITZ system, it is frequently observed that we use only a small subset of total available code numbers. Such sparsity in the actual occurrences of code numbers implies that we can design a better system which uses digits of the system more parsimoniously. A 2-dimensional fuzzy ISODATA algorithm is developed to extract the important characteristics for the classification from the set of given parts. Based on the extracted characteristics nd the distances between fuzzy clustering cenetroids, a company-unique classification and coding system can be developed. An example case study for a medium sized machine shop is presented.

• The Journal of the Korea Contents Association
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• v.7 no.10
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• pp.19-26
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• 2007

Since collaborative filtering has used the nearest-neighborhood method based on item preference it cannot only reflect exact contents but also has the problem of sparsity and scalability. The item-based collaborative filtering has been practically used improve these problems. However it still does not reflect attributes of the item. In this paper, we propose the method of associative group using the FP-Tree to solve the problem of existing recommendation system. The proposed makes frequent item and creates association rule by using FP-Tree without occurrence of candidate set. We made the efficient item group using $\alpha-cut$ according to the confidence of the association rule. To estimate the performance, the suggested method is compared with Gibbs Sampling, Expectation Maximization, and K-means in the MovieLens dataset.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.