23,486 research outputs found
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
The effectiveness of biosignal generation and data augmentation with
biosignal generative models based on generative adversarial networks (GANs),
which are a type of deep learning technique, was demonstrated in our previous
paper. GAN-based generative models only learn the projection between a random
distribution as input data and the distribution of training data.Therefore, the
relationship between input and generated data is unclear, and the
characteristics of the data generated from this model cannot be controlled.
This study proposes a method for generating time-series data based on GANs and
explores their ability to generate biosignals with certain classes and
characteristics. Moreover, in the proposed method, latent variables are
analyzed using canonical correlation analysis (CCA) to represent the
relationship between input and generated data as canonical loadings. Using
these loadings, we can control the characteristics of the data generated by the
proposed method. The influence of class labels on generated data is analyzed by
feeding the data interpolated between two class labels into the generator of
the proposed GANs. The CCA of the latent variables is shown to be an effective
method of controlling the generated data characteristics. We are able to model
the distribution of the time-series data without requiring domain-dependent
knowledge using the proposed method. Furthermore, it is possible to control the
characteristics of these data by analyzing the model trained using the proposed
method. To the best of our knowledge, this work is the first to generate
biosignals using GANs while controlling the characteristics of the generated
data
Long-Range Correlation Underlying Childhood Language and Generative Models
Long-range correlation, a property of time series exhibiting long-term
memory, is mainly studied in the statistical physics domain and has been
reported to exist in natural language. Using a state-of-the-art method for such
analysis, long-range correlation is first shown to occur in long CHILDES data
sets. To understand why, Bayesian generative models of language, originally
proposed in the cognitive scientific domain, are investigated. Among
representative models, the Simon model was found to exhibit surprisingly good
long-range correlation, but not the Pitman-Yor model. Since the Simon model is
known not to correctly reflect the vocabulary growth of natural language, a
simple new model is devised as a conjunct of the Simon and Pitman-Yor models,
such that long-range correlation holds with a correct vocabulary growth rate.
The investigation overall suggests that uniform sampling is one cause of
long-range correlation and could thus have a relation with actual linguistic
processes
Handling oversampling in dynamic networks using link prediction
Oversampling is a common characteristic of data representing dynamic
networks. It introduces noise into representations of dynamic networks, but
there has been little work so far to compensate for it. Oversampling can affect
the quality of many important algorithmic problems on dynamic networks,
including link prediction. Link prediction seeks to predict edges that will be
added to the network given previous snapshots. We show that not only does
oversampling affect the quality of link prediction, but that we can use link
prediction to recover from the effects of oversampling. We also introduce a
novel generative model of noise in dynamic networks that represents
oversampling. We demonstrate the results of our approach on both synthetic and
real-world data.Comment: ECML/PKDD 201
Detecting change points in the large-scale structure of evolving networks
Interactions among people or objects are often dynamic in nature and can be
represented as a sequence of networks, each providing a snapshot of the
interactions over a brief period of time. An important task in analyzing such
evolving networks is change-point detection, in which we both identify the
times at which the large-scale pattern of interactions changes fundamentally
and quantify how large and what kind of change occurred. Here, we formalize for
the first time the network change-point detection problem within an online
probabilistic learning framework and introduce a method that can reliably solve
it. This method combines a generalized hierarchical random graph model with a
Bayesian hypothesis test to quantitatively determine if, when, and precisely
how a change point has occurred. We analyze the detectability of our method
using synthetic data with known change points of different types and
magnitudes, and show that this method is more accurate than several previously
used alternatives. Applied to two high-resolution evolving social networks,
this method identifies a sequence of change points that align with known
external "shocks" to these networks
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference
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