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A dynamic network is frequently encountered in various real
industrial applications, such as the Internet of Things. It is
composed of numerous nodes and large-scale dynamic real-time
interactions among them, where each node indicates a specified
entity, each directed link indicates a real-time interaction, and
the strength of an interaction can be quantified as the weight of a
link. As the involved nodes increase drastically, it becomes
impossible to observe their full interactions at each time slot,
making a resultant dynamic network High Dimensional and Incomplete
(HDI). An HDI dynamic network with directed and weighted links,
despite its HDI nature, contains rich knowledge regarding involved
nodes' various behavior patterns. Therefore, it is essential to
study how to build efficient and effective representation learning
models for acquiring useful knowledge. In this book, we first model
a dynamic network into an HDI tensor and present the basic latent
factorization of tensors (LFT) model. Then, we propose four
representative LFT-based network representation methods. The first
method integrates the short-time bias, long-time bias and
preprocessing bias to precisely represent the volatility of network
data. The second method utilizes a
proportion-al-integral-derivative controller to construct an
adjusted instance error to achieve a higher convergence rate. The
third method considers the non-negativity of fluctuating network
data by constraining latent features to be non-negative and
incorporating the extended linear bias. The fourth method adopts an
alternating direction method of multipliers framework to build a
learning model for implementing representation to dynamic networks
with high preciseness and efficiency.
Latent factor analysis models are an effective type of machine
learning model for addressing high-dimensional and sparse matrices,
which are encountered in many big-data-related industrial
applications. The performance of a latent factor analysis model
relies heavily on appropriate hyper-parameters. However, most
hyper-parameters are data-dependent, and using grid-search to tune
these hyper-parameters is truly laborious and expensive in
computational terms. Hence, how to achieve efficient
hyper-parameter adaptation for latent factor analysis models has
become a significant question.This is the first book to focus on
how particle swarm optimization can be incorporated into latent
factor analysis for efficient hyper-parameter adaptation, an
approach that offers high scalability in real-world industrial
applications. The book will help students, researchers and
engineers fully understand the basic methodologies of
hyper-parameter adaptation via particle swarm optimization in
latent factor analysis models. Further, it will enable them to
conduct extensive research and experiments on the real-world
applications of the content discussed.
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