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Geometric Spanner of Objects Under L1 Distance
"... Abstract. Geometric spanner is a fundamental structure in computational geometry and plays an important role in many geometric networks design applications. In this paper, we consider the following generalized geometric spanner problem under L1 distance: Given a set of disjoint objects S, find a sp ..."
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Abstract. Geometric spanner is a fundamental structure in computational geometry and plays an important role in many geometric networks design applications. In this paper, we consider the following generalized geometric spanner problem under L1 distance: Given a set of disjoint objects S, find a
Approximating L1distances between mixture distributions using random projections
, 804
"... We consider the problem of computing L1distances between every pair of probability densities from a given family. We point out that the technique of Cauchy random projections [Ind06] in this context turns into stochastic integrals with respect to Cauchy motion. For piecewiselinear densities these ..."
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We consider the problem of computing L1distances between every pair of probability densities from a given family. We point out that the technique of Cauchy random projections [Ind06] in this context turns into stochastic integrals with respect to Cauchy motion. For piecewiselinear densities
Similarity of Color Images
, 1995
"... We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L 1 , L 2 , or L1 distance between two cumulative color histograms can be used to define a similarity mea ..."
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Cited by 495 (2 self)
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We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L 1 , L 2 , or L1 distance between two cumulative color histograms can be used to define a similarity
L1DISTANCE FOR ADDITIVE PROCESSES WITH TIMEHOMOGENEOUS LÉVY MEASURES
"... Abstract. We give an explicit bound for the L1distance between two additive processes of local characteristics (fj(·), σ 2(·), νj), j = 1, 2. The cases σ = 0 and σ(·)> 0 are both treated. We allow ν1 and ν2 to be timehomogeneous Lévy measures, possibly with infinite variation. Some examples of ..."
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Abstract. We give an explicit bound for the L1distance between two additive processes of local characteristics (fj(·), σ 2(·), νj), j = 1, 2. The cases σ = 0 and σ(·)> 0 are both treated. We allow ν1 and ν2 to be timehomogeneous Lévy measures, possibly with infinite variation. Some examples
Kmeans clustering of proportional data using L1 distance
, 2008
"... We present a new L1distancebased kmeans clustering algorithm to address the challenge of clustering highdimensional proportional vectors. The new algorithm explicitly incorporates proportionality constraints in the computation of the cluster centroids, resulting in reduced L1 error rates. We com ..."
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Cited by 2 (0 self)
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We present a new L1distancebased kmeans clustering algorithm to address the challenge of clustering highdimensional proportional vectors. The new algorithm explicitly incorporates proportionality constraints in the computation of the cluster centroids, resulting in reduced L1 error rates. We
On L1distance between first exit times from two regions ∗†
, 2008
"... First exit times from regions and their dependence on variations of boundaries are discussed for diffusion processes. The paper presents an estimate of L1distance between exit times from two regions via expectations of exit times. Abbreviated title: On L1distance between first exit times It is kno ..."
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First exit times from regions and their dependence on variations of boundaries are discussed for diffusion processes. The paper presents an estimate of L1distance between exit times from two regions via expectations of exit times. Abbreviated title: On L1distance between first exit times
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
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Cited by 2182 (27 self)
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of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distancebased and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page:
Results 1  10
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988,897