Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sequential Logistic Principal Component Analysis (SLPCA): Dimensional
  Reduction in Streaming Multivariate Binary-State System   Sequential or online dimensional reduction is of interests due to the
explosion of streaming data based applications and the requirement of adaptive
statistical modeling, in many emerging fields, such as the modeling of energy
end-use profile. Principal Component Analysis (PCA), is the classical way of
dimensional reduction. However, traditional Singular Value Decomposition (SVD)
based PCA fails to model data which largely deviates from Gaussian
distribution. The Bregman Divergence was recently introduced to achieve a
generalized PCA framework. If the random variable under dimensional reduction
follows Bernoulli distribution, which occurs in many emerging fields, the
generalized PCA is called Logistic PCA (LPCA). In this paper, we extend the
batch LPCA to a sequential version (i.e. SLPCA), based on the sequential convex
optimization theory. The convergence property of this algorithm is discussed
compared to the batch version of LPCA (i.e. BLPCA), as well as its performance
in reducing the dimension for multivariate binary-state systems. Its
application in building energy end-use profile modeling is also investigated.

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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an