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2007
Histograms are data objects that are commonly used to characterize media objects like image, video, audio etc. Symbolic Data Analysis (SDA) is a field which deals with extracting knowledge and relationship from such complex data objects. The current research scenario of SDA has contributions related to dimensionality reduction of interval kind data ...
R. Pradeep Kumar, P. Nagabhushan
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Histograms are data objects that are commonly used to characterize media objects like image, video, audio etc. Symbolic Data Analysis (SDA) is a field which deals with extracting knowledge and relationship from such complex data objects. The current research scenario of SDA has contributions related to dimensionality reduction of interval kind data ...
R. Pradeep Kumar, P. Nagabhushan
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Geodesic PCA versus Log-PCA of Histograms in the Wasserstein Space
SIAM Journal on Scientific Computing, 2018This paper is concerned with the statistical analysis of datasets whose elements are random histograms. For the purpose of learning principal modes of variation from such data, we consider the issue of computing the principal component analysis (PCA) of ...
Elsa Cazelles+4 more
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On proximity between PCA in the frequency domain and usual PCA
Statistics, 2006The principal components analysis (PCA) in the frequency domain of a stationary p-dimensional time series (X n ) n∈ℤ leads to a summarizing time series written as a linear combination series X′ n =∑ m C m ° X n−m . Therefore, we observe that, when the coefficients C m , m≠0, are close to 0, this PCA is close to the usual PCA, that is the PCA in the ...
Sylvie Viguier-Pla, Alain Boudou
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The equivalence of two-dimensional PCA to line-based PCA
Pattern Recognition Letters, 2005The state-of-the-art in human face recognition is the subspace methods originated by the Principal Component Analysis (PCA), the Eigenfaces of the facial images. Recently, a technique called Two-dimensional PCA (2DPCA) was proposed for human face representation and recognition.
Xiao Wang+3 more
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The American Statistician, 1986
Dr. First studied the Big Forest. Some 100 sites were chosen through skilled use of random number tables. A trained crew of five graduate students visited each site, recording (a) native tree biomass, (b) exotic tree biomass, (c) native shrub biomass, (d) exotic shrub biomass, and (e) total Forbes biomass (all Forbes being native).
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Dr. First studied the Big Forest. Some 100 sites were chosen through skilled use of random number tables. A trained crew of five graduate students visited each site, recording (a) native tree biomass, (b) exotic tree biomass, (c) native shrub biomass, (d) exotic shrub biomass, and (e) total Forbes biomass (all Forbes being native).
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Microsurgical anastomosis with the ‘PCA’ technique
Journal of Plastic, Reconstructive & Aesthetic Surgery, 2008In this study, we introduce a new microvascular anastomosis technique called 'PCA' (Posterior wall first--Continuous interrupted--Airborne). The conventional microvascular anastomosis (CI), with single interrupted stitches, requires a long time to be completed, as each suture must be tied before starting the following stitch.
CIGNA, EMANUELE+5 more
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The Clinical Journal of Pain, 1990
Patients (n = 120) undergoing major orthopedic (e.g., total hip replacement), urologic (e.g., radical prostatectomy), or gynecologic (e.g., total abdominal hysterectomy) procedures were randomly assigned to receive either morphine or oxymorphone postoperatively using a patient-controlled analgesic (PCA) delivery system.
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Patients (n = 120) undergoing major orthopedic (e.g., total hip replacement), urologic (e.g., radical prostatectomy), or gynecologic (e.g., total abdominal hysterectomy) procedures were randomly assigned to receive either morphine or oxymorphone postoperatively using a patient-controlled analgesic (PCA) delivery system.
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PCA-Based Edge-Preserving Features for Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2017Xudong Kang+3 more
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Intelligent bearing fault diagnosis using PCA–DBN framework
Neural computing & applications (Print), 2019Jing Zhu, T. Hu, B. Jiang, Xinzhe Yang
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