32,026 research outputs found
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Similarity-based approaches represent a promising direction for time series
analysis. However, many such methods rely on parameter tuning, and some have
shortcomings if the time series are multivariate (MTS), due to dependencies
between attributes, or the time series contain missing data. In this paper, we
address these challenges within the powerful context of kernel methods by
proposing the robust \emph{time series cluster kernel} (TCK). The approach
taken leverages the missing data handling properties of Gaussian mixture models
(GMM) augmented with informative prior distributions. An ensemble learning
approach is exploited to ensure robustness to parameters by combining the
clustering results of many GMM to form the final kernel.
We evaluate the TCK on synthetic and real data and compare to other
state-of-the-art techniques. The experimental results demonstrate that the TCK
is robust to parameter choices, provides competitive results for MTS without
missing data and outstanding results for missing data.Comment: 23 pages, 6 figure
Capturing Evolution Genes for Time Series Data
The modeling of time series is becoming increasingly critical in a wide
variety of applications. Overall, data evolves by following different patterns,
which are generally caused by different user behaviors. Given a time series, we
define the evolution gene to capture the latent user behaviors and to describe
how the behaviors lead to the generation of time series. In particular, we
propose a uniform framework that recognizes different evolution genes of
segments by learning a classifier, and adopt an adversarial generator to
implement the evolution gene by estimating the segments' distribution.
Experimental results based on a synthetic dataset and five real-world datasets
show that our approach can not only achieve a good prediction results (e.g.,
averagely +10.56% in terms of F1), but is also able to provide explanations of
the results.Comment: a preprint version. arXiv admin note: text overlap with
arXiv:1703.10155 by other author
Unimanual versus bimanual motor imagery classifiers for assistive and rehabilitative brain computer interfaces
Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet electroencephalography (EEG) based assistive and rehabilitative brain computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time. In this study we present a classifier which discriminates between uni-and bimanual MI. Ten able bodied participants took part in cue based motor execution (ME) and MI tasks of the left (L), right (R) and both (B) hands. A 32 channel EEG was recorded. Three linear discriminant analysis classifiers, based on MI of L-B, B-R and B--L hands were created, with features based on wide band Common Spatial Patterns (CSP) 8-30 Hz, and band specifics Common Spatial Patterns (CSPb). Event related desynchronization (ERD) was significantly stronger during bimanual compared to unimanual ME on both hemispheres. Bimanual MI resulted in bilateral parietally shifted ERD of similar intensity to unimanual MI. The average classification accuracy for CSP and CSPb was comparable for L-R task (73±9% and 75±10% respectively) and for L-B task (73±11% and 70±9% respectively). However, for R-B task (67±3% and 72±6% respectively) it was significantly higher for CSPb (p=0.0351). Six participants whose L-R classification accuracy exceeded 70% were included in an on-line task a week later, using the unmodified offline CSPb classifier, achieving 69±3% and 66±3% accuracy for the L-R and R-B tasks respectively. Combined uni and bimanual BCI could be used for restoration of motor function of highly disabled patents and for motor rehabilitation of patients with motor deficits
A Chronology of International Business Cycles Through Non-parametric Decoding
This paper introduces a new empirical strategy for the characterization of business cycles. It combines non-parametric decoding methods that classify a series into expansions and recessions but does not require specification of the underlying stochastic process generating the data. It then uses network analysis to combine the signals obtained from different economic indicators to generate a unique chronology. These methods generate a record of peak and trough dates comparable, and in one sense superior, to the NBERâs own chronology. The methods are then applied to 22 OECD countries to obtain a global business cycle chronology.decoding, hierarchical factor segmentation, network analysis, business cycles
Segmentation of ultrasound images of thyroid nodule for assisting fine needle aspiration cytology
The incidence of thyroid nodule is very high and generally increases with the
age. Thyroid nodule may presage the emergence of thyroid cancer. The thyroid
nodule can be completely cured if detected early. Fine needle aspiration
cytology is a recognized early diagnosis method of thyroid nodule. There are
still some limitations in the fine needle aspiration cytology, and the
ultrasound diagnosis of thyroid nodule has become the first choice for
auxiliary examination of thyroid nodular disease. If we could combine medical
imaging technology and fine needle aspiration cytology, the diagnostic rate of
thyroid nodule would be improved significantly. The properties of ultrasound
will degrade the image quality, which makes it difficult to recognize the edges
for physicians. Image segmentation technique based on graph theory has become a
research hotspot at present. Normalized cut (Ncut) is a representative one,
which is suitable for segmentation of feature parts of medical image. However,
how to solve the normalized cut has become a problem, which needs large memory
capacity and heavy calculation of weight matrix. It always generates over
segmentation or less segmentation which leads to inaccurate in the
segmentation. The speckle noise in B ultrasound image of thyroid tumor makes
the quality of the image deteriorate. In the light of this characteristic, we
combine the anisotropic diffusion model with the normalized cut in this paper.
After the enhancement of anisotropic diffusion model, it removes the noise in
the B ultrasound image while preserves the important edges and local details.
This reduces the amount of computation in constructing the weight matrix of the
improved normalized cut and improves the accuracy of the final segmentation
results. The feasibility of the method is proved by the experimental results.Comment: 15pages,13figure
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