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DigitHist
2017
Proceedings of the VLDB Endowment
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By combining multi-dimensional and one-dimensional histograms along regular grids of different resolutions, DigitHist provides an accurate and reliable histogram approach for multi-dimensional data. ...
We propose DigitHist, a histogram summary for selectivity estimation on multi-dimensional data with tight error bounds. ...
histogram H and, for each dimension i, a one-dimensional marginal histogram M i . 2. ...
doi:10.14778/3137628.3137658
fatcat:grfijk44wfb2bmeqchhbiitzie
Page 266 of Behavior Research Methods Vol. 27, Issue 2
[page]
1995
Behavior Research Methods
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The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
266 YU AND BEHRENS
VARIATIONS IN GRAPHICS FOR VISUALIZATIONS
One-Dimensional Graphs
The histogram is perhaps the most common graphic for displaying the distribution of a single variable. ...
Two-Dimensional Graphs
Bivariate data are usually presented in a scatterplot, which is also subject to the bandwidth problem. ...
An information theoretic histogram for single dimensional selectivity estimation
2005
Proceedings of the 2005 ACM symposium on Applied computing - SAC '05
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This conclusion demonstrates that the entropy histograms are an excellent choice of summary structure for selectivity estimation with respect to the state-of-the-art. ...
The entropy histograms outperformed all other methods on 4 out of 9 real datasets and tied for first on another two. ...
We demonstrate that these histograms represent an excellent choice of summary structure for selectivity estimation with respect to the stateof-the-art. ...
doi:10.1145/1066677.1066831
dblp:conf/sac/GiannellaS05
fatcat:wh73pjhoivb6xjsu6ol4pysema
An experimental evaluation of large scale GBDT systems
2019
Proceedings of the VLDB Endowment
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Our theoretical and experimental results provide a guideline on choosing a proper data management policy for a given workload. ...
Based on the analysis, we further propose a novel distributed GBDT system named Vero, which adopts the unexplored composition of vertical partitioning and row-store and suits for many large-scale cases ...
Except for histogram construction, there are two other phases in GBDT, which are split finding and node splitting. ...
doi:10.14778/3342263.3342273
fatcat:h3lo7wel25fp3niclkoi2mvrf4
Efficient Indexing of High Dimensional Normalized Histograms
[chapter]
2003
Lecture Notes in Computer Science
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This paper addresses the problem of indexing high dimensional normalized histogram data, i.e., D-dimensional feature vectors H where D i=1 Hi = 1. ...
We show that the performance of similarity queries for normalized histogram data can be significantly improved by exploiting such properties within a simple indexing framework. ...
In fact, d L2 (p, q) ≤ √ 2, for any two normalized histograms p and qindependent of their dimensionality. ...
doi:10.1007/978-3-540-45227-0_59
fatcat:45y6kf3fw5cvvivjhlboaco3ju
Detection and Classification
[chapter]
2005
Classification, Parameter Estimation and State Estimation
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We describe a new region descriptor and apply it to two problems, object detection and texture classification. ...
We describe a new region descriptor and apply it to two problems, object detection and texture classification. ...
We construct three separate 64 bin histograms for hue, saturation and value since it is not practical to construct a joint histogram. ...
doi:10.1002/0470090154.ch2
fatcat:zxo57xv24rdahlrcx4mnybgpqm
Detection and Classification
[chapter]
2007
Applied Iterative Methods
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We describe a new region descriptor and apply it to two problems, object detection and texture classification. ...
We describe a new region descriptor and apply it to two problems, object detection and texture classification. ...
We construct three separate 64 bin histograms for hue, saturation and value since it is not practical to construct a joint histogram. ...
doi:10.1201/b10651-41
fatcat:hdyxxwzrezavvcluzqane5s54q
Detection and Classification
[chapter]
2017
Classification, Parameter Estimation and State Estimation
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We describe a new region descriptor and apply it to two problems, object detection and texture classification. ...
We describe a new region descriptor and apply it to two problems, object detection and texture classification. ...
We construct three separate 64 bin histograms for hue, saturation and value since it is not practical to construct a joint histogram. ...
doi:10.1002/9781119152484.ch3
fatcat:ekf4yqfvafcatlh3uxddvq7qzi
13 - Detection and Classification
2005
IEEE/SP 13th Workshop on Statistical Signal Processing, 2005
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We describe a new region descriptor and apply it to two problems, object detection and texture classification. ...
We describe a new region descriptor and apply it to two problems, object detection and texture classification. ...
We construct three separate 64 bin histograms for hue, saturation and value since it is not practical to construct a joint histogram. ...
doi:10.1109/ssp.2005.1628662
fatcat:xfsju5l2pvf5pcmey4hv2zsv7u
Region Covariance: A Fast Descriptor for Detection and Classification
[chapter]
2006
Lecture Notes in Computer Science
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We describe a fast method for computation of covariances based on integral images. ...
We describe a new region descriptor and apply it to two problems, object detection and texture classification. ...
We construct three separate 64 bin histograms for hue, saturation and value since it is not practical to construct a joint histogram. ...
doi:10.1007/11744047_45
fatcat:3erjiyb4rngndal7ih54cmspia
A compact space decomposition for effective metric indexing
2005
Pattern Recognition Letters
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In this aspect our structure is unbeaten. We finish with a discussion of the role of unbalancing in metric space searching, and how it permits trading memory space for construction time. ...
In this context, an index is a data structure that speeds up proximity queries. However, indexes lose their efficiency as the intrinsic data dimensionality increases. ...
Our first experiment tries to determine the best choice among (p1) − (p5). Figure 3 shows the results using two different choices for m * (12 and 100) and r * (1/4 and 1/8 of the maximum distance). ...
doi:10.1016/j.patrec.2004.11.014
fatcat:5ogwhgm62zcfxgksid2cet57au
Upsampling for Improved Multidimensional Attribute Space Clustering of Multifield Data
2018
Information
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Without interpolation, the analysis was highly sensitive to the histogram cell sizes, yielding inaccurate clustering for improper choices: Large histogram cells result in no cluster separation, while clusters ...
Thus, sufficiently high number of data points can be generated, overcoming the curse of dimensionality for this particular type of multidimensional data. ...
The cluster tree constructed for this histogram has depth 9 and contains 19 nodes, see Figure 13e . ...
doi:10.3390/info9070156
fatcat:tullazdmi5gq3c3jna53st72ta
Simple estimation of absolute free energies for biomolecules
2006
Journal of Chemical Physics
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We present a method for calculating the absolute free energy that employs a simple construction of an exactly computable reference system which possesses high overlap with the state of interest. ...
for leucine dipeptide in implicit solvent. ...
ACKNOWLEDGMENTS The authors would like to thank Edward Lyman, Ronald White, Srinath Cheluvarajah, and Hagai Meirovitch for many fruitful discussions. ...
doi:10.1063/1.2174008
pmid:16542066
fatcat:e4vytptkhbdu7lkztjffel7zee
Network analysis using entropy component analysis
2017
Journal of Complex Networks
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Since our entropy is defined in terms of vertex degree values defining an edge, we can histogram the edge entropy using a multi-dimensional array for both undirected and directed networks. ...
of high-dimensional data, as an alternative to classical PCA for component analysis. ...
For undirected graphs the edges are specified by the single degree values for the two participating vertices, and the histogram array is two-dimensional. ...
doi:10.1093/comnet/cnx045
fatcat:sshd7wyzqfaozpzxrapcsaybfq
NNH: Improving Performance of Nearest-Neighbor Searches Using Histograms
[chapter]
2004
Lecture Notes in Computer Science
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Our intensive experiments show that nearest neighbor histograms can be efficiently constructed and maintained, and when used in conjunction with a variety of algorithms for NN search, they can improve ...
In this paper we propose a novel technique, called NNH ("Nearest Neighbor Histograms"), which uses specific histogram structures to improve the performance of NN search algorithms. ...
We provided a complete specification of such histogram structures, showing how to efficiently and accurately construct them, how to incrementally maintain them under dynamic updates, and how to utilize ...
doi:10.1007/978-3-540-24741-8_23
fatcat:z6xp34cccfbixm2nmeokhkxrmi
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