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  • Title/Summary/Keyword: Matrix factorization

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Legendre Tau Method for the 2-D Stokes Problem

  • Jun, SeRan;Kang, Sungkwon;Kwon, YongHoon
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.4 no.2
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    • pp.111-133
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    • 2000
  • A Legendre spectral tau approximation scheme for solving the two-dimensional stationary incompressible Stokes equations is considered. Based on the vorticity-stream function formulation and variational forms, boundary value and normal derivative of vorticity are computed. A factorization technique for matrix stems based on the Schur decomposition is derived. Several numerical experiments are performed.

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NMF for Motor Imagery EEG Classification (NMF를 이용한 Motor Imagery 뇌파 분류)

  • Lee Hye-Kyoung;Cichocki Andrezej;Choi Seung-Jin
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06b
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    • pp.34-36
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    • 2006
  • In this paper we present a method of feature extraction for motor imagery single trial EEG classification, where we exploit nonnegative matrix factorization (NMF) to select discriminative features in the time-frequency representation of EEG. Experimental results with motor Imagery EEG data in BCI competition 2003. show that the method indeed finds meaningful EEG features automatically, while some existing methods should undergo cross-validation to find them.

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NMF-Feature Extraction for Sound Classification (소리 분류를 위한 NMF특징 추출)

  • Yong-Choon Cho;Seungin Choi;Sung-Yang Bang
    • Proceedings of the Korean Information Science Society Conference
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    • 2003.10a
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    • pp.4-6
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    • 2003
  • A holistic representation, such as sparse ceding or independent component analysis (ICA), was successfully applied to explain early auditory processing and sound classification. In contrast, Part-based representation is an alternative way of understanding object recognition in brain. In this paper. we employ the non-negative matrix factorization (NMF)[1]which learns parts-based representation for sound classification. Feature extraction methods from spectrogram using NMF are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.

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Digital Signage service through Customer Behavior pattern analysis

  • Shin, Min-Chan;Park, Jun-Hee;Lee, Ji-Hoon;Moon, Nammee
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.9
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    • pp.53-62
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    • 2020
  • Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.

Emotion Transition Model based Music Classification Scheme for Music Recommendation (음악 추천을 위한 감정 전이 모델 기반의 음악 분류 기법)

  • Han, Byeong-Jun;Hwang, Een-Jun
    • Journal of IKEEE
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    • v.13 no.2
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    • pp.159-166
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    • 2009
  • So far, many researches have been done to retrieve music information using static classification descriptors such as genre and mood. Since static classification descriptors are based on diverse content-based musical features, they are effective in retrieving similar music in terms of such features. However, human emotion or mood transition triggered by music enables more effective and sophisticated query in music retrieval. So far, few works have been done to evaluate the effect of human mood transition by music. Using formal representation of such mood transitions, we can provide personalized service more effectively in the new applications such as music recommendation. In this paper, we first propose our Emotion State Transition Model (ESTM) for describing human mood transition by music and then describe a music classification and recommendation scheme based on the ESTM. In the experiment, diverse content-based features were extracted from music clips, dimensionally reduced by NMF (Non-negative Matrix Factorization, and classified by SVM (Support Vector Machine). In the performance analysis, we achieved average accuracy 67.54% and maximum accuracy 87.78%.

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Statistical Voice Activity Detection Using Probabilistic Non-Negative Matrix Factorization (확률적 비음수 행렬 인수분해를 사용한 통계적 음성검출기법)

  • Kim, Dong Kook;Shin, Jong Won;Kwon, Kisoo;Kim, Nam Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.8
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    • pp.851-858
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    • 2016
  • This paper presents a new statistical voice activity detection (VAD) based on the probabilistic interpretation of nonnegative matrix factorization (NMF). The objective function of the NMF using Kullback-Leibler divergence coincides with the negative log likelihood function of the data if the distribution of the data given the basis and encoding matrices is modeled as Poisson distributions. Based on this probabilistic NMF, the VAD is constructed using the likelihood ratio test assuming that speech and noise follow Poisson distributions. Experimental results show that the proposed approach outperformed the conventional Gaussian model-based and NMF-based methods at 0-15 dB signal-to-noise ratio simulation conditions.

Preliminary Source Apportionment of Ambient VOCs Measured in Seoul Metropolitan Area by Positive Matrix Factorization (PMF를 이용한 수도권지역 VOCs의 배출원 추정)

  • Han J. S.;Moon K. J.;Kim R. H.;Shin S. A.;Hong Y. D.;Jung I. R.
    • Journal of Korean Society for Atmospheric Environment
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    • v.22 no.1
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    • pp.85-97
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    • 2006
  • The PAMS data collected at four sites in Seoul metropolitan area in 2004 were analyzed using the positive matrix factorization (PMF) technique, in order to identify the possible sources and estimate their contributions to ambient VOCs. Ten sources were then resolved at Jeongdong, Bulgwang, Yangpyeong, and Seokmo, including vehicle exhaust, LPG vehicle, petroleum evaporation, coating, solvent, asphalt, LNG, Industry & heating, open burning, and biogenic source. The PMF analysis results showed that vehicle exhaust commonly contributed the largest portion of the predicted total VOCs mass concentration, more than 30% at four sites. The contribution of other resolved sources were significantly different according to the characteristics of site location. In the case of Jeongdong and bulgwang located in urban area, various anthropogenic sources such as coating, solvent, asphalt, residual LPG, and petroleum evaporation contributed about 40% of total VOCs mass. On the other hand, at yangpyeong and Seokmo located in rural and remote area, the portion of these anthropogenic sources was reduced to less than 30% and the contribution of natural sources including open burning and biogenic source clearly observed. These results were considerably corresponding to the emission inventory investigated in this region.

Automatic Email Multi-category Classification Using Dynamic Category Hierarchy and Non-negative Matrix Factorization (비음수 행렬 분해와 동적 분류 체계를 사용한 자동 이메일 다원 분류)

  • Park, Sun;An, Dong-Un
    • Journal of KIISE:Software and Applications
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    • v.37 no.5
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    • pp.378-385
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    • 2010
  • The explosive increase in the use of email has made to need email classification efficiently and accurately. Current work on the email classification method have mainly been focused on a binary classification that filters out spam-mails. This methods are based on Support Vector Machines, Bayesian classifiers, rule-based classifiers. Such supervised methods, in the sense that the user is required to manually describe the rules and keyword list that is used to recognize the relevant email. Other unsupervised method using clustering techniques for the multi-category classification is created a category labels from a set of incoming messages. In this paper, we propose a new automatic email multi-category classification method using NMF for automatic category label construction method and dynamic category hierarchy method for the reorganization of email messages in the category labels. The proposed method in this paper, a large number of emails are managed efficiently by classifying multi-category email automatically, email messages in their category are reorganized for enhancing accuracy whenever users want to classify all their email messages.

A Boolean Factorization Using an Extended Two-cube Matrix (확장된 2-큐브 행렬을 이용한 부울 분해식 산출)

  • Kwon, Oh-Hyeong;Oh, Im-Geol
    • Journal of the Korea Computer Industry Society
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    • v.8 no.4
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    • pp.229-236
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    • 2007
  • A factored form is a sum of products of sums of products, ..., of arbitrary depth. Factoring is the process of deriving a parenthesized form with the smallest number of literals from a two-level form of a logic expression. The factored form is not unique and described as either algebraic or Boolean. A Boolean factored form contains fewer number of literals than an algebraic factored form. In this paper, we present a new method for a Boolean factorization. The key idea is to identify two-cube Boolean subexpressions from given two-level logic expression and to extract divisor/quotient pairs. Then, we derive extended divisor/quotient pairs, where their quotients are not cube-free, from the generated divisor/quotients pairs. We generate quotient/quotient pairs from divisor/quotient pairs and extended divisor/quotient pairs. Using the pairs, we make a matrix to generate Boolean factored form based on a technique of rectangle covering.

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Schur Algorithm for Sub-bottom Profiling (해저지층 탐사를 위한 Schur 알고리즘)

  • Bae, Jinho;Lee, Chong Hyun;Kim, Hoeyong;Cho, Jung-Hong
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.9
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    • pp.156-163
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    • 2013
  • In this paper, we propose an algorithm for estimating media characteristics of sea water and subbottom multi-layers. The proposed algorithm for estimating reflection coefficients, uses a transmitted signal and reflected signal obtained from multiple layers of various shape and structure, and the algorithm is called Schur algorithm. The algorithm is efficient in estimating the reflection coefficients since it finds solution by converting the given inverse scattering problem into matrix factorization. To verify the proposed algorithm, we generate a transmit signal and reflected signal obtained from lattice filter model for sea water and subbottom of multi-level non-homogeneous layers, and then find that the proposed algorithm can estimate reflection coefficients efficiently.