• Title/Summary/Keyword: Subspace

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APPROXIMATION PROPERTIES OF PAIRS OF SUBSPACES

  • Lee, Keun Young
    • Bulletin of the Korean Mathematical Society
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    • v.56 no.3
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    • pp.563-568
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    • 2019
  • This study is concerned with the approximation properties of pairs. For ${\lambda}{\geq}1$, we prove that given a Banach space X and a closed subspace $Z_0$, if the pair ($X,Z_0$) has the ${\lambda}$-bounded approximation property (${\lambda}$-BAP), then for every ideal Z containing $Z_0$, the pair ($Z,Z_0$) has the ${\lambda}$-BAP; further, if Z is a closed subspace of X and the pair (X, Z) has the ${\lambda}$-BAP, then for every separable subspace $Y_0$ of X, there exists a separable closed subspace Y containing $Y_0$ such that the pair ($Y,Y{\cap}Z$) has the ${\lambda}$-BAP. We also prove that if Z is a separable closed subspace of X, then the pair (X, Z) has the ${\lambda}$-BAP if and only if for every separable subspace $Y_0$ of X, there exists a separable closed subspace Y containing $Y_0{\cup}Z$ such that the pair (Y, Z) has the ${\lambda}$-BAP.

Signal Subspace-based Voice Activity Detection Using Generalized Gaussian Distribution (일반화된 가우시안 분포를 이용한 신호 준공간 기반의 음성검출기법)

  • Um, Yong-Sub;Chang, Joon-Hyuk;Kim, Dong Kook
    • The Journal of the Acoustical Society of Korea
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    • v.32 no.2
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    • pp.131-137
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    • 2013
  • In this paper we propose an improved voice activity detection (VAD) algorithm using statistical models in the signal subspace domain. A uncorrelated signal subspace is generated using embedded prewhitening technique and the statistical characteristics of the noisy speech and noise are investigated in this domain. According to the characteristics of the signals in the signal subspace, a new statistical VAD method using GGD (Generalized Gaussian Distribution) is proposed. Experimental results show that the proposed GGD-based approach outperforms the Gaussian-based signal subspace method at 0-15 dB SNR simulation conditions.

A Square-Root Forward Backward Correlation-based Projection Approximation for Subspace Tracking (신호부공간 추정 성능 향상을 위한 전후방 상관과 제곱근행렬 갱신을 이용한 COPAST(correlation-based projection approximation for subspace-tracking) 알고리즘 연구)

  • Lim, June-Seok;Pyeon, Yong-Kug
    • 전자공학회논문지 IE
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    • v.48 no.1
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    • pp.7-15
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    • 2011
  • In this paper, we propose a correlation-based subspace estimation technique, which is called square-root forward/backward correlation-based projection approximation subspace tracking(SRFB-COPAST). The SRFB-COPAST utilizes the forward and backward correlation matrix as well as square-root recursive matrix update in projection approximation approach to develop the subspace tracking algorithm. With the projection approximation, the square-root recursive FB-COPAST is presented. The proposed algorithm has the better performance than the recently developed COPAST method.

SEQUENTIAL EM LEARNING FOR SUBSPACE ANALYSIS

  • Park, Seungjin
    • Proceedings of the IEEK Conference
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    • pp.698-701
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    • 2002
  • Subspace analysis (which includes PCA) seeks for feature subspace (which corresponds to the eigenspace), given multivariate input data and has been widely used in computer vision and pattern recognition. Typically data space belongs to very high dimension, but only a few principal components need to be extracted. In this paper I present a fast sequential algorithm for subspace analysis or tracking. Useful behavior of the algorithm is confirmed by numerical experiments.

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THE HYPERINVARIANT SUBSPACE PROBLEM FOR QUASI-n-HYPONORMAL OPERATORS

  • Kim, An-Hyun;Kwon, Eun-Young
    • Communications of the Korean Mathematical Society
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    • v.22 no.3
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    • pp.383-389
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    • 2007
  • In this paper we examine the hyperinvariant subspace problem for quasi-n-hyponormal operators. The main result on this problem is as follows. If T = N + K is such that N is a quasi-n-hyponormal operator whose spectrum contains an exposed arc and K belongs to the Schatten p-ideal then T has a non-trivial hyperinvariant subspace.

Signal-Subspace-Based Simple Adaptive Array and Performance Analysis (신호 부공간에 기초한 간단한 적응 어레이 및 성능분석)

  • Choi, Yang-Ho
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.47 no.6
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    • pp.162-170
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    • 2010
  • Adaptive arrays reject interferences while preserving the desired signal, exploiting a priori information on its arrival angle. Subspace-based adaptive arrays, which adjust their weight vectors in the signal subspace, have the advantages of fast convergence and robustness to steering vector errors, as compared with the ones in the full dimensional space. However, the complexity of theses subspace-based methods is high because the eigendecomposition of the covariance matrix is required. In this paper, we present a simple subspace-based method based on the PASTd (projection approximation subspace tracking with deflation). The orignal PASTd algorithm is modified such that eigenvectora are orthogonal to each other. The proposed method allows us to significantly reduce the computational complexity, substantially having the same performance as the beamformer with the direct eigendecomposition. In addition to the simple beamforming method, we present theoretical analyses on the SINR (signal-to-interference plus noise ratio) of subspace beamformers to see their behaviors.

An ICA-Based Subspace Scanning Algorithm to Enhance Spatial Resolution of EEG/MEG Source Localization (뇌파/뇌자도 전류원 국지화의 공간분해능 향상을 위한 독립성분분석 기반의 부분공간 탐색 알고리즘)

  • Jung, Young-Jin;Kwon, Ki-Woon;Im, Chang-Hwan
    • Journal of Biomedical Engineering Research
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    • v.31 no.6
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    • pp.456-463
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    • 2010
  • In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.

Developing an Ensemble Classifier for Bankruptcy Prediction (부도 예측을 위한 앙상블 분류기 개발)

  • Min, Sung-Hwan
    • Journal of the Korea Industrial Information Systems Research
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    • v.17 no.7
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    • pp.139-148
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    • 2012
  • An ensemble of classifiers is to employ a set of individually trained classifiers and combine their predictions. It has been found that in most cases the ensembles produce more accurate predictions than the base classifiers. Combining outputs from multiple classifiers, known as ensemble learning, is one of the standard and most important techniques for improving classification accuracy in machine learning. An ensemble of classifiers is efficient only if the individual classifiers make decisions as diverse as possible. Bagging is the most popular method of ensemble learning to generate a diverse set of classifiers. Diversity in bagging is obtained by using different training sets. The different training data subsets are randomly drawn with replacement from the entire training dataset. The random subspace method is an ensemble construction technique using different attribute subsets. In the random subspace, the training dataset is also modified as in bagging. However, this modification is performed in the feature space. Bagging and random subspace are quite well known and popular ensemble algorithms. However, few studies have dealt with the integration of bagging and random subspace using SVM Classifiers, though there is a great potential for useful applications in this area. The focus of this paper is to propose methods for improving SVM performance using hybrid ensemble strategy for bankruptcy prediction. This paper applies the proposed ensemble model to the bankruptcy prediction problem using a real data set from Korean companies.

SMOOTH FUZZY CLOSURE AND TOPOLOGICAL SPACES

  • Kim, Yong Chan
    • Korean Journal of Mathematics
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    • v.7 no.1
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    • pp.11-25
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    • 1999
  • We will define a smooth fuzzy closure space and a subspace of it. We will investigate relationships between smooth fuzzy closure spaces and smooth fuzzy topological spaces. In particular, we will show that a subspace of a smooth fuzzy topological space can be obtained by the subspace of the smooth fuzzy closure space induced by it.

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Time-Varying Subspace Tracking Algorithm for Nonstationary DOA Estimation in Passive Sensor Array

  • Lim, Junseok;Song, Joonil;Pyeon, Yongkug;Sung, Koengmo
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.1E
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    • pp.7-13
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    • 2001
  • In this paper we propose a new subspace tracking algorithm based on the PASTd (Projection Approximation Subspace Tracking with deflation). The algorithm is obtained via introducing the variable forgetting factor which adapts itself to the time-varying subspace environments. The tracking capability of the proposed algorithm is demonstrated by computer simulations in an abruptly changing DOA scenario. The estimation results of the variable forgetting factor PASTd(VFF-PASTd) outperform those of the PASTd in the nonstationary case as well as in the stationary case.

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