119 research outputs found
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables
Understanding nonlinear dynamical systems (NLDSs) is challenging in a variety
of engineering and scientific fields. Dynamic mode decomposition (DMD), which
is a numerical algorithm for the spectral analysis of Koopman operators, has
been attracting attention as a way of obtaining global modal descriptions of
NLDSs without requiring explicit prior knowledge. However, since existing DMD
algorithms are in principle formulated based on the concatenation of scalar
observables, it is not directly applicable to data with dependent structures
among observables, which take, for example, the form of a sequence of graphs.
In this paper, we formulate Koopman spectral analysis for NLDSs with structures
among observables and propose an estimation algorithm for this problem. This
method can extract and visualize the underlying low-dimensional global dynamics
of NLDSs with structures among observables from data, which can be useful in
understanding the underlying dynamics of such NLDSs. To this end, we first
formulate the problem of estimating spectra of the Koopman operator defined in
vector-valued reproducing kernel Hilbert spaces, and then develop an estimation
procedure for this problem by reformulating tensor-based DMD. As a special case
of our method, we propose the method named as Graph DMD, which is a numerical
algorithm for Koopman spectral analysis of graph dynamical systems, using a
sequence of adjacency matrices. We investigate the empirical performance of our
method by using synthetic and real-world data.Comment: 34 pages with 4 figures, Published in Neural Networks, 201
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies
We propose a neural network-based model for nonlinear dynamics in continuous
time that can impose inductive biases on decay rates and/or frequencies.
Inductive biases are helpful for training neural networks especially when
training data are small. The proposed model is based on the Koopman operator
theory, where the decay rate and frequency information is used by restricting
the eigenvalues of the Koopman operator that describe linear evolution in a
Koopman space. We use neural networks to find an appropriate Koopman space,
which are trained by minimizing multi-step forecasting and backcasting errors
using irregularly sampled time-series data. Experiments on various time-series
datasets demonstrate that the proposed method achieves higher forecasting
performance given a single short training sequence than the existing methods
Stable Invariant Models via Koopman Spectra
Weight-tied models have attracted attention in the modern development of
neural networks. The deep equilibrium model (DEQ) represents infinitely deep
neural networks with weight-tying, and recent studies have shown the potential
of this type of approach. DEQs are needed to iteratively solve root-finding
problems in training and are built on the assumption that the underlying
dynamics determined by the models converge to a fixed point. In this paper, we
present the stable invariant model (SIM), a new class of deep models that in
principle approximates DEQs under stability and extends the dynamics to more
general ones converging to an invariant set (not restricted in a fixed point).
The key ingredient in deriving SIMs is a representation of the dynamics with
the spectra of the Koopman and Perron--Frobenius operators. This perspective
approximately reveals stable dynamics with DEQs and then derives two variants
of SIMs. We also propose an implementation of SIMs that can be learned in the
same way as feedforward models. We illustrate the empirical performance of SIMs
with experiments and demonstrate that SIMs achieve comparative or superior
performance against DEQs in several learning tasks
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
In anomaly detection, the degree of irregularity is often summarized as a
real-valued anomaly score. We address the problem of attributing such anomaly
scores to input features for interpreting the results of anomaly detection. We
particularly investigate the use of the Shapley value for attributing anomaly
scores of semi-supervised detection methods. We propose a characteristic
function specifically designed for attributing anomaly scores. The idea is to
approximate the absence of some features by locally minimizing the anomaly
score with regard to the to-be-absent features. We examine the applicability of
the proposed characteristic function and other general approaches for
interpreting anomaly scores on multiple datasets and multiple anomaly detection
methods. The results indicate the potential utility of the attribution methods
including the proposed one
Learning Dynamics Models with Stable Invariant Sets
Invariance and stability are essential notions in dynamical systems study,
and thus it is of great interest to learn a dynamics model with a stable
invariant set. However, existing methods can only handle the stability of an
equilibrium. In this paper, we propose a method to ensure that a dynamics model
has a stable invariant set of general classes such as limit cycles and line
attractors. We start with the approach by Manek and Kolter (2019), where they
use a learnable Lyapunov function to make a model stable with regard to an
equilibrium. We generalize it for general sets by introducing projection onto
them. To resolve the difficulty of specifying a to-be stable invariant set
analytically, we propose defining such a set as a primitive shape (e.g.,
sphere) in a latent space and learning the transformation between the original
and latent spaces. It enables us to compute the projection easily, and at the
same time, we can maintain the model's flexibility using various invertible
neural networks for the transformation. We present experimental results that
show the validity of the proposed method and the usefulness for long-term
prediction
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