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Performance from Sparse Data

by Joseph P. Elm, Joseph P. Elm , 2011
"... search and development center. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Department of Defense. This report was prepared for the ..."
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search and development center. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Department of Defense. This report was prepared for the

Efficient sparse coding algorithms

by Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y. Ng - In NIPS , 2007
"... Sparse coding provides a class of algorithms for finding succinct representations of stimuli; given only unlabeled input data, it discovers basis functions that capture higher-level features in the data. However, finding sparse codes remains a very difficult computational problem. In this paper, we ..."
Abstract - Cited by 445 (14 self) - Add to MetaCart
Sparse coding provides a class of algorithms for finding succinct representations of stimuli; given only unlabeled input data, it discovers basis functions that capture higher-level features in the data. However, finding sparse codes remains a very difficult computational problem. In this paper, we

Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets

by Jeffrey Scott Vitter, Min Wang
"... Computing multidimensional aggregates in high dimensions is a performance bottleneck for many OLAP applications. Obtaining the exact answer to an aggregation query can be prohibitively expensive in terms of time and/or storage space in a data warehouse environment. It is advantageous to have fast, a ..."
Abstract - Cited by 198 (3 self) - Add to MetaCart
, approximate answers to OLAP aggregation queries. In this paper, we present anovel method that provides approximate answers to high-dimensional OLAP aggregation queries in massive sparse data sets in a time-efficient and space-efficient manner. We construct a compact data cube, which is an approximate

A message ferrying approach for data delivery in sparse mobile ad hoc networks

by Wenrui Zhao, Mostafa Ammar, Ellen Zegura - In Proc. of ACM Mobihoc , 2004
"... Mobile Ad Hoc Networks (MANETs) provide rapidly deployable and self-configuring network capacity required in many critical applications, e.g., battlefields, disaster relief and wide area sensing. In this paper we study the problem of efficient data delivery in sparse MANETs where network partitions ..."
Abstract - Cited by 498 (14 self) - Add to MetaCart
Mobile Ad Hoc Networks (MANETs) provide rapidly deployable and self-configuring network capacity required in many critical applications, e.g., battlefields, disaster relief and wide area sensing. In this paper we study the problem of efficient data delivery in sparse MANETs where network partitions

Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging

by Michael Lustig, David Donoho, John M. Pauly - MAGNETIC RESONANCE IN MEDICINE 58:1182–1195 , 2007
"... The sparsity which is implicit in MR images is exploited to significantly undersample k-space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial finit ..."
Abstract - Cited by 538 (11 self) - Add to MetaCart
finite-differences or their wavelet coefficients. According to the recently developed mathematical theory of compressedsensing, images with a sparse representation can be recovered from randomly undersampled k-space data, provided an appropriate nonlinear recovery scheme is used. Intuitively, artifacts

Handling Sparse Data by Successive Abstraction

by Christer Samuelsson - In Proc. of the International Conference on Computational Linguistics (COLING'96 , 1996
"... A general, practical method for handling sparse data that avoids held-out data and iterative rccstimation is derived from first principles. It has been tested on a part-of-speech tagging task and out- performed (deleted) interpolation with context-independent weights, even when the latter use ..."
Abstract - Cited by 15 (2 self) - Add to MetaCart
A general, practical method for handling sparse data that avoids held-out data and iterative rccstimation is derived from first principles. It has been tested on a part-of-speech tagging task and out- performed (deleted) interpolation with context-independent weights, even when the latter

K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

by Michal Aharon, et al. , 2006
"... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and inc ..."
Abstract - Cited by 935 (41 self) - Add to MetaCart
that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. The update of the dictionary columns is combined with an update of the sparse representations, thereby accelerating convergence. The K-SVD algorithm

A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants

by Radford Neal, Geoffrey E. Hinton - Learning in Graphical Models , 1998
"... . The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the d ..."
Abstract - Cited by 993 (18 self) - Add to MetaCart
. The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect

Column-Stores For Wide and Sparse Data

by unknown authors
"... While it is generally accepted that data warehouses and OLAP workloads are excellent applications for column-stores, this paper speculates that column-stores may well be suited for additional applications. In particular we observe that column-stores do not see a performance degradation when storing ..."
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extremely wide tables, and column-stores handle sparse data very well. These two properties lead us to conjecture that column-stores may be good storage layers for Semantic Web data, XML data, and data with GEM-style schemas. 1.

Column Stores For Wide and Sparse Data

by unknown authors
"... While it is generally accepted that data warehouses and OLAP workloads are excellent applications for column-stores, this paper speculates that column-stores may well be suited for additional applications. In particular we observe that column-stores do not see a performance degradation when storing ..."
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extremely wide tables, and column-stores handle sparse data very well. These two properties lead us to conjecture that column-stores may be good storage layers for Semantic Web data, XML data, and data with GEM-style schemas. 1.
Results 11 - 20 of 11,934