12,423 research outputs found
On Randomly Projected Hierarchical Clustering with Guarantees
Hierarchical clustering (HC) algorithms are generally limited to small data
instances due to their runtime costs. Here we mitigate this shortcoming and
explore fast HC algorithms based on random projections for single (SLC) and
average (ALC) linkage clustering as well as for the minimum spanning tree
problem (MST). We present a thorough adaptive analysis of our algorithms that
improve prior work from by up to a factor of for a
dataset of points in Euclidean space. The algorithms maintain, with
arbitrary high probability, the outcome of hierarchical clustering as well as
the worst-case running-time guarantees. We also present parameter-free
instances of our algorithms.Comment: This version contains the conference paper "On Randomly Projected
Hierarchical Clustering with Guarantees'', SIAM International Conference on
Data Mining (SDM), 2014 and, additionally, proofs omitted in the conference
versio
Searching for Jets in Heavy Ion Collisions
Jet quenching measurements using leading particles and their correlations
suffer from known biases, which can be removed via direct reconstruction of
jets in central heavy ion collisions. In this talk, we discuss several modern
jet reconstruction algorithms and background subtraction techniques that are
appropriate to heavy ion collisions.Comment: Proceedings of the 24th Winter Workshop on Nuclear Dynamics, South
Padre Island, Texas, 5-12 April 200
Pedestrian detection in uncontrolled environments using stereo and biometric information
A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios
Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering
Hierarchical clustering is a popular method for analyzing data which
associates a tree to a dataset. Hartigan consistency has been used extensively
as a framework to analyze such clustering algorithms from a statistical point
of view. Still, as we show in the paper, a tree which is Hartigan consistent
with a given density can look very different than the correct limit tree.
Specifically, Hartigan consistency permits two types of undesirable
configurations which we term over-segmentation and improper nesting. Moreover,
Hartigan consistency is a limit property and does not directly quantify
difference between trees.
In this paper we identify two limit properties, separation and minimality,
which address both over-segmentation and improper nesting and together imply
(but are not implied by) Hartigan consistency. We proceed to introduce a merge
distortion metric between hierarchical clusterings and show that convergence in
our distance implies both separation and minimality. We also prove that uniform
separation and minimality imply convergence in the merge distortion metric.
Furthermore, we show that our merge distortion metric is stable under
perturbations of the density.
Finally, we demonstrate applicability of these concepts by proving
convergence results for two clustering algorithms. First, we show convergence
(and hence separation and minimality) of the recent robust single linkage
algorithm of Chaudhuri and Dasgupta (2010). Second, we provide convergence
results on manifolds for topological split tree clustering
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