9,280 research outputs found
DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams
Similarity matching and join of time series data streams has gained a lot of
relevance in today's world that has large streaming data. This process finds
wide scale application in the areas of location tracking, sensor networks,
object positioning and monitoring to name a few. However, as the size of the
data stream increases, the cost involved to retain all the data in order to aid
the process of similarity matching also increases. We develop a novel framework
to addresses the following objectives. Firstly, Dimension reduction is
performed in the preprocessing stage, where large stream data is segmented and
reduced into a compact representation such that it retains all the crucial
information by a technique called Multi-level Segment Means (MSM). This reduces
the space complexity associated with the storage of large time-series data
streams. Secondly, it incorporates effective Similarity Matching technique to
analyze if the new data objects are symmetric to the existing data stream. And
finally, the Pruning Technique that filters out the pseudo data object pairs
and join only the relevant pairs. The computational cost for MSM is O(l*ni) and
the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction
Factor. We have performed exhaustive experimental trials to show that the
proposed framework is both efficient and competent in comparison with earlier
works.Comment: 20 pages,8 figures, 6 Table
Temporal validation of particle filters for video tracking
This is the author’s version of a work that was accepted for publication in Journal
Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal
Computer Vision and Image Understanding, 131 (2015) DOI: 10.1016/j.cviu.2014.06.016A novel approach to determine adaptively the temporal consistency of Particle Filters.The proposed method is demonstrated on online performance evaluation of tracking.Temporal consistency is modeled by convolutions of mixtures of Gamma distributions.The proposed method does not need thresholds and can be used on large datasets. We present an approach for determining the temporal consistency of Particle Filters in video tracking based on model validation of their uncertainty over sliding windows. The filter uncertainty is related to the consistency of the dispersion of the filter hypotheses in the state space. We learn an uncertainty model via a mixture of Gamma distributions whose optimum number is selected by modified information-based criteria. The time-accumulated model is estimated as the sequential convolution of the uncertainty model. Model validation is performed by verifying whether the output of the filter belongs to the convolution model through its approximated cumulative density function. Experimental results and comparisons show that the proposed approach improves both precision and recall of competitive approaches such as Gaussian-based online model extraction, bank of Kalman filters and empirical thresholding. We combine the proposed approach with a state-of-the-art online performance estimator for video tracking and show that it improves accuracy compared to the same estimator with manually tuned thresholds while reducing the overall computational cost.This work was partially supported by the Spanish Government
(EventVideo, TEC2011-25995) and by the EU Crowded Environments
monitoring for Activity Understanding and Recognition
(CENTAUR, FP7-PEOPLE-2012-IAPP) project under GA number
324359. Most of the work reported in this paper was done at the
Centre for Intelligent Sensing in Queen Mary University of London
Tight Bounds for Adversarially Robust Streams and Sliding Windows via Difference Estimators
In the adversarially robust streaming model, a stream of elements is
presented to an algorithm and is allowed to depend on the output of the
algorithm at earlier times during the stream. In the classic insertion-only
model of data streams, Ben-Eliezer et. al. (PODS 2020, best paper award) show
how to convert a non-robust algorithm into a robust one with a roughly
factor overhead. This was subsequently improved to a
factor overhead by Hassidim et. al. (NeurIPS 2020, oral
presentation), suppressing logarithmic factors. For general functions the
latter is known to be best-possible, by a result of Kaplan et. al. (CRYPTO
2021). We show how to bypass this impossibility result by developing data
stream algorithms for a large class of streaming problems, with no overhead in
the approximation factor. Our class of streaming problems includes the most
well-studied problems such as the -heavy hitters problem, -moment
estimation, as well as empirical entropy estimation. We substantially improve
upon all prior work on these problems, giving the first optimal dependence on
the approximation factor.
As in previous work, we obtain a general transformation that applies to any
non-robust streaming algorithm and depends on the so-called flip number.
However, the key technical innovation is that we apply the transformation to
what we call a difference estimator for the streaming problem, rather than an
estimator for the streaming problem itself. We then develop the first
difference estimators for a wide range of problems. Our difference estimator
methodology is not only applicable to the adversarially robust model, but to
other streaming models where temporal properties of the data play a central
role. (Abstract shortened to meet arXiv limit.)Comment: FOCS 202
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