• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.1-18
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• 2016

Collaborative Filtering (CF) predicts the focal user's preference for particular item based on user's preference rating data and recommends items for the similar users by using them. It is a popular technique for the personalization in e-commerce to reduce information overload. However, it has some limitations including sparsity and scalability problems. In this paper, we use a method to integrate social network information into collaborative filtering in order to mitigate the sparsity and scalability problems which are major limitations of typical collaborative filtering and reflect the user's qualitative and emotional information in recommendation process. In this paper, we use a novel recommendation algorithm which is integrated with collaborative filtering by using Social SVD++ algorithm which considers social network information in SVD++, an extension algorithm that can reflect implicit information in singular value decomposition (SVD). In particular, this study will evaluate the performance of the model by reflecting the real-world user's social network information in the recommendation process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10A
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• pp.980-986
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• 2008

Spatial multiuser access is investigated for the reverse link of multiuser multiple-input multiple-output (MIMO) systems. In particular, we consider two alternative a aches to spatial multiuser access that adopt the same detection algorithm at the base station: one is a closed-loop approach based on singular value decomposition (SVD) of the channel matrix, whereas the other is an open-loop approach based in space-time block coding (STBC). We develop multiuser detection algorithms for these two spatial multiuser access schemes based on the minimum mean square error (MMSE) criterion. Then, we compare the bit error rate (BER) performance of the two schemes and a single-user MIMO scheme. Interestingly, it is found that the STBC approach can provide much better BER performance than the SVD approach as well as than a single-user MIMO scheme.

• KSBB Journal
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• v.16 no.4
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• pp.381-388
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• 2001

A program for integrated gene expression profile analysis such as hierarchical clustering, K-means, fuzzy c-means, self-organizing map(SOM), principal component analysis(PCA), and singular value decomposition(SVD) was made for DNA chip data anlysis by using Matlab. It also contained the normalization method of gene expression input data. The integrated data anlysis program could be effectively used in DNA chip data analysis and help researchers to get more comprehensive analysis view on gene expression data of their own.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.165-170
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• 2013

When we build up a 3D model from the given 2D images, linear algorithms are often used to reduce computational cost or for initialization of nonlinear algorithms. However, contemporary linear algorithms have apparently linear structures, but virtually they are implemented using SVD. The SVD is also implemented using numerical analysis algorithms that need initialization. Moreover, solutions using SVD are more difficult to analyze than closed-form solutions. To avoid from such inconvenient numerical analysis algorithms of the contemporary methods and for convenient analysis of solutions, this paper proposes a convenient linear method that produces a closed-form solution.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.705-712
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• 2005

Biplot is a graphical display of the rows and columns of an n${\times}$p data matrix. In particular, Gabriel (1995) suggested the MANOVA biplot using singular value decomposition (SVD) with the averages of response variables according to treatment groups. But his biplot may cause wrong results by disregarding them when there exist covariate effects. In this paper, we will provide the MANCOA biplot based on the SVD with the parameter estimates for MANCOVA model when there exist covariate effects.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.285-290
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• 2005

Biplot is a graphical display of the rows and columns an $n{\time}p$ data matrix. In particular, Gabriel(1981) suggested The MANOVA BIPLOT using singular value decomposition (SVD) with the averages of response variables according to treatment groups. But his biplot may cause wrong results by disregarding them when there exists covariate effects. In this paper, we will provide the MANCOVA BIPLOT based on the SVD with the parameter estimates for MANCOVA model when there exist covariate effects.

• Journal of Distribution Science
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• v.14 no.9
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• pp.25-29
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• 2016

Purpose - Finding top K persuaders in consumer network is an important problem in marketing. Recently, a new method of computing persuasion scores, interpreted as fixed point or stable distribution for given persuasion probabilities, was proposed. Top K persuaders are chosen according to the computed scores. This research proposed a new definition of persuasion scores relaxing some conditions on the matrix of probabilities, and a method to identify top K persuaders based on the defined scores. Research design, data, and methodology - A new method of computing top K persuaders is computed by singular value decomposition (SVD) of the matrix which represents persuasion probabilities between entities. Results - By testing a randomly generated instance, it turns out that the proposed method is essentially different from the previous study sharing a similar idea. Conclusions - The proposed method is shown to be valid with respect to both theoretical analysis and empirical test. However, this method is limited to the category of persuasion scores relying on the matrix-form of persuasion probabilities. In addition, the strength of the method should be evaluated via additional experiments, e.g., using real instances, different benchmark methods, efficient numerical methods for SVD, and other decomposition methods such as NMF.

• The Journal of Engineering Geology
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• v.28 no.3
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• pp.465-473
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• 2018

The application of singular value decomposition (SVD) filtering is examined for attenuation of the ground-roll in land seismic data. Prior to the SVD computation to seek singular values containing the highly correlatable reflection energy, processing steps such as automatic gain control, elevation and refraction statics, NMO correction, and residual statics are performed to enhance the horizontal correlationships and continuities of reflections. Optimal parameters of SVD filtering are effectively chosen with diagnostic display of inverse NMO (INMO) corrected CSP (common shot point) gather. On the field data with dispersion of ground-roll overwhelmed, continuities of reflection events are much improved by SVD filtering than f-k filtering by eliminating the ground-roll with preserving the low-frequency reflections. This is well explained in the average amplitude spectra of the f-k and SVD filtered data. The reflectors including horizontal layer of the reservoir are much clearer on the stack section, with laminated events by SVD filtering and subsequent processing steps of spiking deconvolution and time-variant spectral whitening.

• Journal of Communications and Networks
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• v.17 no.5
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• pp.491-498
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• 2015

Recognition of radar emitter signals is one of core elements in radar reconnaissance systems. A novel method based on singular value decomposition (SVD) and the main ridge slice of ambiguity function (AF) is presented for attaining a higher correct recognition rate of radar emitter signals in case of low signal-to-noise ratio. This method calculates the AF of the sorted signal and ascertains the main ridge slice envelope. To improve the recognition performance, SVD is employed to eliminate the influence of noise on the main ridge slice envelope. The rotation angle and symmetric Holder coefficients of the main ridge slice envelope are extracted as the elements of the feature vector. And kernel fuzzy c-means clustering is adopted to analyze the feature vector and classify different types of radar signals. Simulation results indicate that the feature vector extracted by the proposed method has satisfactory aggregation within class, separability between classes, and stability. Compared to existing methods, the proposed feature recognition method can achieve a higher correct recognition rate.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.9
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• pp.431-438
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• 2001

This paper present experimental results of source identification for non-minimum phase system. Generally, a causal linear system may be described by matrix form. The inverse problem is considered as a matrix inversion. Direct inverse method can\`t be applied for a non-minimum phase system, the reason is that the system has ill-conditioning. Therefore, in this study to execute an effective inversion, SVD inverse technique is introduced. In a Non-minimum phase system, its system matrix may be singular or near-singular and has one more very small singular values. These very small singular values have information about a phase of the system and ill-conditioning. Using this property we could solve the ill-conditioned problem of the system and then verified it for the practical system(cantilever beam). The experimental results show that SVD inverse technique works well for non-minimum phase system.