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KodairaSpencer theory of gravity and exact results for quantum string amplitudes
 Commun. Math. Phys
, 1994
"... We develop techniques to compute higher loop string amplitudes for twisted N = 2 theories with ĉ = 3 (i.e. the critical case). An important ingredient is the discovery of an anomaly at every genus in decoupling of BRST trivial states, captured to all orders by a master anomaly equation. In a particu ..."
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Cited by 540 (59 self)
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We develop techniques to compute higher loop string amplitudes for twisted N = 2 theories with ĉ = 3 (i.e. the critical case). An important ingredient is the discovery of an anomaly at every genus in decoupling of BRST trivial states, captured to all orders by a master anomaly equation. In a
7 Extensive Comparative Study: Robust Face Recognition
"... Face recognition is a computer based person identification technique based on arithmetical and numerical features obtained from face images. In a face recognition system, illumination has been a great problem. In this work, light variation is tried to dominate to increase recognition rate. To improv ..."
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. To improve the face recognition rate further, a novel technique is adopted, where facial images are vertically divided into two equal halves and D2DPCA dimension reduction technique is applied in each halves. Normalization is performed by two different techniques, which are MinMax and ZScore recognition
Graphcut textures: Image and video synthesis using graph cuts
 ACM Transactions on Graphics, SIGGRAPH 2003
, 2003
"... This banner was generated by merging the source images in Figure 6 using our interactive texture merging technique. In this paper we introduce a new algorithm for image and video texture synthesis. In our approach, patch regions from a sample image or video are transformed and copied to the output a ..."
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Cited by 490 (9 self)
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. Unlike dynamic programming, our graph cut technique for seam optimization is applicable in any dimension. We specifically explore it in 2D and 3D to perform video texture synthesis in addition to regular image synthesis. We present approximative offset search techniques that work well in conjunction
Efficient ray tracing of volume data
 ACM Transactions on Graphics
, 1990
"... Volume rendering is a technique for visualizing sampled scalar or vector fields of three spatial dimensions without fitting geometric primitives to the data. A subset of these techniques generates images by computing 2D projections of a colored semitransparent volume, where the color and opacity at ..."
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Cited by 392 (5 self)
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Volume rendering is a technique for visualizing sampled scalar or vector fields of three spatial dimensions without fitting geometric primitives to the data. A subset of these techniques generates images by computing 2D projections of a colored semitransparent volume, where the color and opacity
A Survey of Dimension Reduction Techniques
, 2002
"... this paper, we assume that we have n observations, each being a realization of the p dimensional random variable x = (x 1 , . . . , x p ) with mean E(x) = = ( 1 , . . . , p ) and covariance matrix E{(x )(x = # pp . We denote such an observation matrix by X = i,j : 1 p, 1 ..."
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Cited by 141 (0 self)
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this paper, we assume that we have n observations, each being a realization of the p dimensional random variable x = (x 1 , . . . , x p ) with mean E(x) = = ( 1 , . . . , p ) and covariance matrix E{(x )(x = # pp . We denote such an observation matrix by X = i,j : 1 p, 1 n}. If i and # i = # (i,i) denote the mean and the standard deviation of the ith random variable, respectively, then we will often standardize the observations x i,j by (x i,j i )/ # i , where i = x i = 1/n j=1 x i,j , and # i = 1/n j=1 (x i,j x i )
Principal manifolds and nonlinear dimensionality reduction via tangent space alignment
 SIAM JOURNAL ON SCIENTIFIC COMPUTING
, 2004
"... Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized ..."
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Cited by 261 (15 self)
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Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized
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
Dimension Reduction by Local Principal Component Analysis
, 1997
"... Reducing or eliminating statistical redundancy between the components of highdimensional vector data enables a lowerdimensional representation without significant loss of information. Recognizing the limitations of principal component analysis (PCA), researchers in the statistics and neural networ ..."
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Cited by 131 (0 self)
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network communities have developed nonlinear extensions of PCA. This article develops a local linear approach to dimension reduction that provides accurate representations and is fast to compute. We exercise the algorithms on speech and image data, and compare performance with PCA and with neural network
Incremental Online Learning in High Dimensions
 Neural Computation
, 2005
"... Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally e ..."
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Cited by 164 (19 self)
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dimensional spaces and review the various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. The properties of LWPR are that it i) learns rapidly with second order learning methods based on incremental training, ii) uses statistically sound stochastic leaveoneout cross
Kmeans Clustering via Principal Component Analysis
, 2004
"... Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. Kmeans clustering is a commonly used data clustering for performing unsupervised learning tasks. Here we ..."
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Cited by 201 (5 self)
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Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. Kmeans clustering is a commonly used data clustering for performing unsupervised learning tasks. Here we
Results 1  10
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