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Here, we provide a simple example of using low-rank matrix recovery algorithms for high-dimensional data processing.
First, the dimension of the PIR data stream is low, which avoids the high-dimensional data processing.
The output of the PIR sensors is low-dimensional temporal data stream, which avoids the high-dimensional data processing.
The problem formulated in (3) shows the fundamental task of the research on matrix recovery for high-dimensional data processing, which can be efficiently solved by some existing algorithms including augmented Lagrange multipliers (ALM) algorithm and accelerated proximal gradient (APG) algorithm [90].
Projection pursuit classification model (PPC) is a kind of statistics for high-dimensional data processing and analysis and its main thought is to project the high-dimensional data to low-dimensional space and reflect projection value of high-dimensional data structure or feature by low-dimensional space.
The usage of high-dimensional data complicates data processing in social network area.
The chapter also presents a different method by looking into the design of a neural network as an approximation problem in a high-dimensional space because data processing in a radial basis function neural network is quite different from standard supervised or unsupervised learning techniques.
The technique employed is based on the measurements of the radial component of the magnetic induction vector using a Hall probe at the points located on the three dimensional surface with subsequent data processing using the Laplace equation.
Sparse representation methods including compressive sensing have been widely studied recently in applied mathematics and signal/image processing for their advantages in processing high dimensional data [ 13, 14].
Cosmic ray correction and background subtraction were performed on the two-dimensional image before further data processing.
As a result, the most obvious merit of our SensFall is its low dimensional input data stream for data processing.
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