1,422 research outputs found
Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers
We present a new approach for online handwritten signature classification and
verification based on descriptors stemming from Information Theory. The
proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher
Information evaluated over the Bandt and Pompe symbolization of the horizontal
and vertical coordinates of signatures. These six features are easy and fast to
compute, and they are the input to an One-Class Support Vector Machine
classifier. The results produced surpass state-of-the-art techniques that
employ higher-dimensional feature spaces which often require specialized
software and hardware. We assess the consistency of our proposal with respect
to the size of the training sample, and we also use it to classify the
signatures into meaningful groups.Comment: Submitted to PLOS On
Dynamical variety of shapes in financial multifractality
The concept of multifractality offers a powerful formal tool to filter out
multitude of the most relevant characteristics of complex time series. The
related studies thus far presented in the scientific literature typically limit
themselves to evaluation of whether or not a time series is multifractal and
width of the resulting singularity spectrum is considered a measure of the
degree of complexity involved. However, the character of the complexity of time
series generated by the natural processes usually appears much more intricate
than such a bare statement can reflect. As an example, based on the long-term
records of S&P500 and NASDAQ - the two world leading stock market indices - the
present study shows that they indeed develop the multifractal features, but
these features evolve through a variety of shapes, most often strongly
asymmetric, whose changes typically are correlated with the historically most
significant events experienced by the world economy. Relating at the same time
the index multifractal singularity spectra to those of the component stocks
that form this index reflects the varying degree of correlations involved among
the stocks.Comment: 26 pages, 10 figure
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection
Dynamic time warping (DTW) is an effective dissimilarity measure in many time
series applications. Despite its popularity, it is prone to noises and
outliers, which leads to singularity problem and bias in the measurement. The
time complexity of DTW is quadratic to the length of time series, making it
inapplicable in real-time applications. In this paper, we propose a novel time
series dissimilarity measure named RobustDTW to reduce the effects of noises
and outliers. Specifically, the RobustDTW estimates the trend and optimizes the
time warp in an alternating manner by utilizing our designed temporal graph
trend filtering. To improve efficiency, we propose a multi-level framework that
estimates the trend and the warp function at a lower resolution, and then
repeatedly refines them at a higher resolution. Based on the proposed
RobustDTW, we further extend it to periodicity detection and outlier time
series detection. Experiments on real-world datasets demonstrate the superior
performance of RobustDTW compared to DTW variants in both outlier time series
detection and periodicity detection
MULTISCALE KERNELS FOR DIFFEOMORPHIC BRAIN IMAGE AND SURFACE MATCHING
Ph.DDOCTOR OF PHILOSOPH
Multidirectional and Topography-based Dynamic-scale Varifold Representations with Application to Matching Developing Cortical Surfaces
The human cerebral cortex is marked by great complexity as well as substantial dynamic changes during early postnatal development. To obtain a fairly comprehensive picture of its age-induced and/or disorder-related cortical changes, one needs to match cortical surfaces to one another, while maximizing their anatomical alignment. Methods that geodesically shoot surfaces into one another as currents (a distribution of oriented normals) and varifolds (a distribution of non-oriented normals) provide an elegant Riemannian framework for generic surface matching and reliable statistical analysis. However, both conventional current and varifold matching methods have two key limitations. First, they only use the normals of the surface to measure its geometry and guide the warping process, which overlooks the importance of the orientations of the inherently convoluted cortical sulcal and gyral folds. Second, the ‘conversion’ of a surface into a current or a varifold operates at a fixed scale under which geometric surface details will be neglected, which ignores the dynamic scales of cortical foldings. To overcome these limitations and improve varifold-based cortical surface registration, we propose two different strategies. The first strategy decomposes each cortical surface into its normal and tangent varifold representations, by integrating principal curvature direction field into the varifold matching framework, thus providing rich information of the orientation of cortical folding and better characterization of the complex cortical geometry. The second strategy explores the informative cortical geometric features to perform a dynamic-scale measurement of the cortical surface that depends on the local surface topography (e.g., principal curvature), thereby we introduce the concept of a topography-based dynamic-scale varifold. We tested the proposed varifold variants for registering 12 pairs of dynamically developing cortical surfaces from 0 to 6 months of age. Both variants improved the matching accuracy in terms of closeness to the target surface and the goodness of alignment with regional anatomical boundaries, when compared with three state-of-the-art methods: (1) diffeomorphic spectral matching, (2) conventional current-based surface matching, and (3) conventional varifold-based surface matching
Adaptive Evolutionary Clustering
In many practical applications of clustering, the objects to be clustered
evolve over time, and a clustering result is desired at each time step. In such
applications, evolutionary clustering typically outperforms traditional static
clustering by producing clustering results that reflect long-term trends while
being robust to short-term variations. Several evolutionary clustering
algorithms have recently been proposed, often by adding a temporal smoothness
penalty to the cost function of a static clustering method. In this paper, we
introduce a different approach to evolutionary clustering by accurately
tracking the time-varying proximities between objects followed by static
clustering. We present an evolutionary clustering framework that adaptively
estimates the optimal smoothing parameter using shrinkage estimation, a
statistical approach that improves a naive estimate using additional
information. The proposed framework can be used to extend a variety of static
clustering algorithms, including hierarchical, k-means, and spectral
clustering, into evolutionary clustering algorithms. Experiments on synthetic
and real data sets indicate that the proposed framework outperforms static
clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox
available at http://tbayes.eecs.umich.edu/xukevin/affec
Automatic classification of deformable shapes
Let be a dataset of smooth 3D-surfaces, partitioned into
disjoint classes , . We show how optimized
diffeomorphic registration applied to large numbers of pairs can provide descriptive feature vectors to implement automatic
classification on , and generate classifiers invariant by rigid
motions in . To enhance accuracy of automatic classification, we
enrich the smallest classes by diffeomorphic interpolation of
smooth surfaces between pairs . We also implement small
random perturbations of surfaces by random flows of smooth
diffeomorphisms . Finally, we test our
automatic classification methods on a cardiology data base of discretized
mitral valve surfaces.Comment: 29 pages; 8 figures; one tabl
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