9,696 research outputs found
Accounting for income distribution trends: A density function decomposition approach
This paper develops methods for decomposing changes in the income distribution using subgroup decompositions of the income density function. Overall changes are related to changes in subgroup shares and changes in subgroup densities, where the latter are broken down further using elementary transformations of individual incomes. These density decompositions are analogous to the widely-used decompositions of inequality indices by population subgroup, except that they summarize multiple features of the income distribution (using graphs), rather than focusing on a specific feature such as dispersion, and are not dependent on the choice of a specific summary index. Nonetheless, since inequality and poverty indices can be expressed as PDF functionals, our density-based methods can also be used to provide numerical decompositions of these. An application of the methods reveals the multi-faceted nature of UK income distribution trends during the 1980s.Income distribution ; Inequality ; density functions ; subgroup decomposition
EEG sleep stages identification based on weighted undirected complex networks
Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks.
Methods
each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks.
Results
In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals.
Conclusions
An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard
Nonlinear dance motion analysis and motion editing using Hilbert-Huang transform
Human motions (especially dance motions) are very noisy, and it is hard to
analyze and edit the motions. To resolve this problem, we propose a new method
to decompose and modify the motions using the Hilbert-Huang transform (HHT).
First, HHT decomposes a chromatic signal into "monochromatic" signals that are
the so-called Intrinsic Mode Functions (IMFs) using an Empirical Mode
Decomposition (EMD) [6]. After applying the Hilbert Transform to each IMF, the
instantaneous frequencies of the "monochromatic" signals can be obtained. The
HHT has the advantage to analyze non-stationary and nonlinear signals such as
human-joint-motions over FFT or Wavelet transform.
In the present paper, we propose a new framework to analyze and extract some
new features from a famous Japanese threesome pop singer group called
"Perfume", and compare it with Waltz and Salsa dance. Using the EMD, their
dance motions can be decomposed into motion (choreographic) primitives or IMFs.
Therefore we can scale, combine, subtract, exchange, and modify those IMFs, and
can blend them into new dance motions self-consistently. Our analysis and
framework can lead to a motion editing and blending method to create a new
dance motion from different dance motions.Comment: 6 pages, 10 figures, Computer Graphics International 2017, Conference
short pape
Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations
The mesoscopic organization of complex systems, from financial markets to the
brain, is an intermediate between the microscopic dynamics of individual units
(stocks or neurons, in the mentioned cases), and the macroscopic dynamics of
the system as a whole. The organization is determined by "communities" of units
whose dynamics, represented by time series of activity, is more strongly
correlated internally than with the rest of the system. Recent studies have
shown that the binary projections of various financial and neural time series
exhibit nontrivial dynamical features that resemble those of the original data.
This implies that a significant piece of information is encoded into the binary
projection (i.e. the sign) of such increments. Here, we explore whether the
binary signatures of multiple time series can replicate the same complex
community organization of the financial market, as the original weighted time
series. We adopt a method that has been specifically designed to detect
communities from cross-correlation matrices of time series data. Our analysis
shows that the simpler binary representation leads to a community structure
that is almost identical with that obtained using the full weighted
representation. These results confirm that binary projections of financial time
series contain significant structural information.Comment: 15 pages, 7 figure
Dynamic Tensor Clustering
Dynamic tensor data are becoming prevalent in numerous applications. Existing
tensor clustering methods either fail to account for the dynamic nature of the
data, or are inapplicable to a general-order tensor. Also there is often a gap
between statistical guarantee and computational efficiency for existing tensor
clustering solutions. In this article, we aim to bridge this gap by proposing a
new dynamic tensor clustering method, which takes into account both sparsity
and fusion structures, and enjoys strong statistical guarantees as well as high
computational efficiency. Our proposal is based upon a new structured tensor
factorization that encourages both sparsity and smoothness in parameters along
the specified tensor modes. Computationally, we develop a highly efficient
optimization algorithm that benefits from substantial dimension reduction. In
theory, we first establish a non-asymptotic error bound for the estimator from
the structured tensor factorization. Built upon this error bound, we then
derive the rate of convergence of the estimated cluster centers, and show that
the estimated clusters recover the true cluster structures with a high
probability. Moreover, our proposed method can be naturally extended to
co-clustering of multiple modes of the tensor data. The efficacy of our
approach is illustrated via simulations and a brain dynamic functional
connectivity analysis from an Autism spectrum disorder study.Comment: Accepted at Journal of the American Statistical Associatio
- …