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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:
Ktheory for operator algebras
 Mathematical Sciences Research Institute Publications
, 1998
"... p. XII line5: since p. 12: I blew this simple formula: should be α = −〈ξ, η〉/〈η, η〉. p. 2 I.1.1.4: The RieszFischer Theorem is often stated this way today, but neither Riesz nor Fischer (who worked independently) phrased it in terms of completeness of the orthogonal system {e int}. If [a, b] is a ..."
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Cited by 558 (0 self)
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space is not σfinite. p. 13: add after I.2.6.16: I.2.6.17. If X is a compact subset of C not containing 0, and k ∈ N, there is in general no bound on the norm of T −1 as T ranges over all operators with ‖T ‖ ≤ k and σ(T) ⊆ X. For example, let Sn ∈ L(l 2) be the truncated shift: Sn(α1, α2,...) = (0
The ratedistortion function for source coding with side information at the decoder
 IEEE Trans. Inform. Theory
, 1976
"... AbstractLet {(X,, Y,J}r = 1 be a sequence of independent drawings of a pair of dependent random variables X, Y. Let us say that X takes values in the finite set 6. It is desired to encode the sequence {X,} in blocks of length n into a binary stream*of rate R, which can in turn be decoded as a seque ..."
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Cited by 1060 (1 self)
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AbstractLet {(X,, Y,J}r = 1 be a sequence of independent drawings of a pair of dependent random variables X, Y. Let us say that X takes values in the finite set 6. It is desired to encode the sequence {X,} in blocks of length n into a binary stream*of rate R, which can in turn be decoded as a
The irreducibility of the space of curves of given genus
 Publ. Math. IHES
, 1969
"... Fix an algebraically closed field k. Let Mg be the moduli space of curves of genus g over k. The main result of this note is that Mg is irreducible for every k. Of course, whether or not M s is irreducible depends only on the characteristic of k. When the characteristic s o, we can assume that k ~ ..."
Efficient time series matching by wavelets
 Proc. of 15th Int'l Conf. on Data Engineering
, 1999
"... Time series stored as feature vectors can be indexed by multidimensional index trees like RTrees for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series to reduce the number of dimensions of the feature vectors. Different transformations like Discrete ..."
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Cited by 286 (1 self)
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Time series stored as feature vectors can be indexed by multidimensional index trees like RTrees for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series to reduce the number of dimensions of the feature vectors. Different transformations like
EpsilonNets and Simplex Range Queries
, 1986
"... We present a new technique for halfspace and simplex range query using O(n) space and O(n a) query time, where a < if(al) +7 for all dimensions d ~2 a(al) + 1 and 7> 0. These bounds are better than those previously published for all d ~ 2. The technique uses random sampling to build a part ..."
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Cited by 282 (7 self)
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We present a new technique for halfspace and simplex range query using O(n) space and O(n a) query time, where a < if(al) +7 for all dimensions d ~2 a(al) + 1 and 7> 0. These bounds are better than those previously published for all d ~ 2. The technique uses random sampling to build a
Sure independence screening for ultrahigh dimensional feature space
, 2006
"... Variable selection plays an important role in high dimensional statistical modeling which nowadays appears in many areas and is key to various scientific discoveries. For problems of large scale or dimensionality p, estimation accuracy and computational cost are two top concerns. In a recent paper, ..."
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Cited by 283 (26 self)
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, Candes and Tao (2007) propose the Dantzig selector using L1 regularization and show that it achieves the ideal risk up to a logarithmic factor log p. Their innovative procedure and remarkable result are challenged when the dimensionality is ultra high as the factor log p can be large and their uniform
Bounded geometries, fractals, and lowdistortion embeddings
"... The doubling constant of a metric space (X; d) is thesmallest value * such that every ball in X can be covered by * balls of half the radius. The doubling dimension of X isthen defined as dim(X) = log2 *. A metric (or sequence ofmetrics) is called doubling precisely when its doubling dimension is ..."
Dimension Reduction in the l1 norm
 in The 43th Annual Symposium on Foundations of Computer Science (FOCS'02
, 2002
"... The JohnsonLindenstrauss Lemma shows that any set of n points in Euclidean space can be mapped linearly down to ) dimensions such that all pairwise distances are distorted by at most 1 + #. We study the following basic question: Does there exist an analogue of the JohnsonLindenstrauss Lemma for t ..."
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Cited by 9 (1 self)
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embeddings. In particular, we show dimensionality reduction theorems for tree metrics, circulardecomposable metrics, and metrics supported on K 2,3 free graphs, giving embeddings into # 1 with constant distortion. Finally, we also present lower bounds on dimension reduction techniques for other # p
On the Impossibility of Dimension Reduction in l_1
 In Proc. 35th Annu. ACM Sympos. Theory Comput
, 2003
"... The JohnsonLindenstrauss Lemma shows that any n points in Euclidean space (with distances measured by the L2 norm) may be mapped down to O((log n)/ep^2) dimensions such that no pairwise distance is distorted by more than a (1 ep) factor. Determining whether such dimension reduction is possible in L ..."
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Cited by 53 (1 self)
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The JohnsonLindenstrauss Lemma shows that any n points in Euclidean space (with distances measured by the L2 norm) may be mapped down to O((log n)/ep^2) dimensions such that no pairwise distance is distorted by more than a (1 ep) factor. Determining whether such dimension reduction is possible
Results 1  10
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2,640