16 research outputs found
Data Mining at NASA: From Theory to Applications
This slide presentation demonstrates the data mining/machine learning capabilities of NASA Ames and Intelligent Data Understanding (IDU) group. This will encompass the work done recently in the group by various group members. The IDU group develops novel algorithms to detect, classify, and predict events in large data streams for scientific and engineering systems. This presentation for Knowledge Discovery and Data Mining 2009 is to demonstrate the data mining/machine learning capabilities of NASA Ames and IDU group. This will encompass the work done re cently in the group by various group members
Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science
The purpose of the New York Workshop on Computer, Earth and Space Sciences is
to bring together the New York area's finest Astronomers, Statisticians,
Computer Scientists, Space and Earth Scientists to explore potential synergies
between their respective fields. The 2011 edition (CESS2011) was a great
success, and we would like to thank all of the presenters and participants for
attending. This year was also special as it included authors from the upcoming
book titled "Advances in Machine Learning and Data Mining for Astronomy". Over
two days, the latest advanced techniques used to analyze the vast amounts of
information now available for the understanding of our universe and our planet
were presented. These proceedings attempt to provide a small window into what
the current state of research is in this vast interdisciplinary field and we'd
like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011
in New York City, Goddard Institute for Space Studie
Certified and fast computations with shallow covariance kernels
Many techniques for data science and uncertainty quantification demand
efficient tools to handle Gaussian random fields, which are defined in terms of
their mean functions and covariance operators. Recently, parameterized Gaussian
random fields have gained increased attention, due to their higher degree of
flexibility. However, especially if the random field is parameterized through
its covariance operator, classical random field discretization techniques fail
or become inefficient. In this work we introduce and analyze a new and
certified algorithm for the low-rank approximation of a parameterized family of
covariance operators which represents an extension of the adaptive cross
approximation method for symmetric positive definite matrices. The algorithm
relies on an affine linear expansion of the covariance operator with respect to
the parameters, which needs to be computed in a preprocessing step using, e.g.,
the empirical interpolation method. We discuss and test our new approach for
isotropic covariance kernels, such as Mat\'ern kernels. The numerical results
demonstrate the advantages of our approach in terms of computational time and
confirm that the proposed algorithm provides the basis of a fast sampling
procedure for parameter dependent Gaussian random fields
Activity representation with motion hierarchies
International audienceComplex activities, e.g., pole vaulting, are composed of a variable number of sub-events connected by complex spatio-temporal relations, whereas simple actions can be represented as sequences of short temporal parts. In this paper, we learn hierarchical representations of activity videos in an unsupervised manner. These hierarchies of mid-level motion components are data-driven decompositions specific to each video. We introduce a spectral divisive clustering algorithm to efficiently extract a hierarchy over a large number of tracklets (i.e., local trajectories). We use this structure to represent a video as an unordered binary tree. We model this tree using nested histograms of local motion features. We provide an efficient positive definite kernel that computes the structural and visual similarity of two hierarchical decompositions by relying on models of their parent-child relations. We present experimental results on four recent challenging benchmarks: the High Five dataset [Patron-Perez et al, 2010], the Olympics Sports dataset [Niebles et al, 2010], the Hollywood 2 dataset [Marszalek et al, 2009], and the HMDB dataset [Kuehne et al, 2011]. We show that pervideo hierarchies provide additional information for activity recognition. Our approach improves over unstructured activity models, baselines using other motion decomposition algorithms, and the state of the art