33,044 research outputs found
A time series distance measure for efficient clustering of input output signals by their underlying dynamics
Starting from a dataset with input/output time series generated by multiple
deterministic linear dynamical systems, this paper tackles the problem of
automatically clustering these time series. We propose an extension to the
so-called Martin cepstral distance, that allows to efficiently cluster these
time series, and apply it to simulated electrical circuits data. Traditionally,
two ways of handling the problem are used. The first class of methods employs a
distance measure on time series (e.g. Euclidean, Dynamic Time Warping) and a
clustering technique (e.g. k-means, k-medoids, hierarchical clustering) to find
natural groups in the dataset. It is, however, often not clear whether these
distance measures effectively take into account the specific temporal
correlations in these time series. The second class of methods uses the
input/output data to identify a dynamic system using an identification scheme,
and then applies a model norm-based distance (e.g. H2, H-infinity) to find out
which systems are similar. This, however, can be very time consuming for large
amounts of long time series data. We show that the new distance measure
presented in this paper performs as good as when every input/output pair is
modelled explicitly, but remains computationally much less complex. The
complexity of calculating this distance between two time series of length N is
O(N logN).Comment: 6 pages, 4 figures, sent in for review to IEEE L-CSS (CDC 2017
option
Nilpotent Approximations of Sub-Riemannian Distances for Fast Perceptual Grouping of Blood Vessels in 2D and 3D
We propose an efficient approach for the grouping of local orientations
(points on vessels) via nilpotent approximations of sub-Riemannian distances in
the 2D and 3D roto-translation groups and . In our distance
approximations we consider homogeneous norms on nilpotent groups that locally
approximate , and which are obtained via the exponential and logarithmic
map on . In a qualitative validation we show that the norms provide
accurate approximations of the true sub-Riemannian distances, and we discuss
their relations to the fundamental solution of the sub-Laplacian on .
The quantitative experiments further confirm the accuracy of the
approximations. Quantitative results are obtained by evaluating perceptual
grouping performance of retinal blood vessels in 2D images and curves in
challenging 3D synthetic volumes. The results show that 1) sub-Riemannian
geometry is essential in achieving top performance and 2) that grouping via the
fast analytic approximations performs almost equally, or better, than
data-adaptive fast marching approaches on and .Comment: 18 pages, 9 figures, 3 tables, in review at JMI
Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency
Subsequence matching has appeared to be an ideal approach for solving many
problems related to the fields of data mining and similarity retrieval. It has
been shown that almost any data class (audio, image, biometrics, signals) is or
can be represented by some kind of time series or string of symbols, which can
be seen as an input for various subsequence matching approaches. The variety of
data types, specific tasks and their partial or full solutions is so wide that
the choice, implementation and parametrization of a suitable solution for a
given task might be complicated and time-consuming; a possibly fruitful
combination of fragments from different research areas may not be obvious nor
easy to realize. The leading authors of this field also mention the
implementation bias that makes difficult a proper comparison of competing
approaches. Therefore we present a new generic Subsequence Matching Framework
(SMF) that tries to overcome the aforementioned problems by a uniform frame
that simplifies and speeds up the design, development and evaluation of
subsequence matching related systems. We identify several relatively separate
subtasks solved differently over the literature and SMF enables to combine them
in straightforward manner achieving new quality and efficiency. This framework
can be used in many application domains and its components can be reused
effectively. Its strictly modular architecture and openness enables also
involvement of efficient solutions from different fields, for instance
efficient metric-based indexes. This is an extended version of a paper
published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201
LocNet: Global localization in 3D point clouds for mobile vehicles
Global localization in 3D point clouds is a challenging problem of estimating
the pose of vehicles without any prior knowledge. In this paper, a solution to
this problem is presented by achieving place recognition and metric pose
estimation in the global prior map. Specifically, we present a semi-handcrafted
representation learning method for LiDAR point clouds using siamese LocNets,
which states the place recognition problem to a similarity modeling problem.
With the final learned representations by LocNet, a global localization
framework with range-only observations is proposed. To demonstrate the
performance and effectiveness of our global localization system, KITTI dataset
is employed for comparison with other algorithms, and also on our long-time
multi-session datasets for evaluation. The result shows that our system can
achieve high accuracy.Comment: 6 pages, IV 2018 accepte
The Bregman Variational Dual-Tree Framework
Graph-based methods provide a powerful tool set for many non-parametric
frameworks in Machine Learning. In general, the memory and computational
complexity of these methods is quadratic in the number of examples in the data
which makes them quickly infeasible for moderate to large scale datasets. A
significant effort to find more efficient solutions to the problem has been
made in the literature. One of the state-of-the-art methods that has been
recently introduced is the Variational Dual-Tree (VDT) framework. Despite some
of its unique features, VDT is currently restricted only to Euclidean spaces
where the Euclidean distance quantifies the similarity. In this paper, we
extend the VDT framework beyond the Euclidean distance to more general Bregman
divergences that include the Euclidean distance as a special case. By
exploiting the properties of the general Bregman divergence, we show how the
new framework can maintain all the pivotal features of the VDT framework and
yet significantly improve its performance in non-Euclidean domains. We apply
the proposed framework to different text categorization problems and
demonstrate its benefits over the original VDT.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
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