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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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It is computationally expensive to compute principal components from scratch at every update or downdate when new data arrive and existing data are truncated from the data matrix frequently. To overcome this limitations, incremental principal component analysis is considered. Specifically, we present a sliding window based efficient incremental principal component computation from a covariance matrix which comprises of two procedures; simultaneous update and downdate of principal components, followed by the rank-one matrix update. Additionally we track the accurate decomposition error and the adaptive numerical rank. Experiments show that the proposed algorithm enables a faster execution speed and no-meaningful decomposition error differences compared to typical incremental principal component analysis algorithms, thereby maintaining a good approximation for the principal components.
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참고문헌 (9)
참고문헌 신청
H. Abdi / 2010 / Principal component analysis / Wiley Interdiscip. Rev. Com-put. Stat. 2(4):433~459
R. Badeau / 2004 / Sliding window adaptive SVD algorithms / IEEE Trans. Signal Process 52(1):1~10
J. L. Barlow / 2005 / Improved Gram-Schmidt type downdating methods / BIT 45(2):259~285
M. Brand / 2006 / Fast low-rank modications of the thin singular value decomposition / Linear Algebra Appl 415(1):20~30
J. R. Bunch / 1978 / Updating the singular value decomposition / Numer. Math 31(2):111~129
R. Badeau / 2004 / Sliding window adaptive SVD algorithms / IEEE Trans. Signal Process 52(1):1~10
J. L. Barlow / 2005 / Improved Gram-Schmidt type downdating methods / BIT 45(2):259~285
M. Brand / 2006 / Fast low-rank modications of the thin singular value decomposition / Linear Algebra Appl 415(1):20~30
J. R. Bunch / 1978 / Updating the singular value decomposition / Numer. Math 31(2):111~129
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