Results 1  10
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143
Diffusion maps and coarsegraining: A unified framework for dimensionality reduction, graph partitioning and data set parameterization
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... We provide evidence that nonlinear dimensionality reduction, clustering and data set parameterization can be solved within one and the same framework. The main idea is to define a system of coordinates with an explicit metric that reflects the connectivity of a given data set and that is robust to ..."
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Cited by 96 (5 self)
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We provide evidence that nonlinear dimensionality reduction, clustering and data set parameterization can be solved within one and the same framework. The main idea is to define a system of coordinates with an explicit metric that reflects the connectivity of a given data set and that is robust to noise. Our construction, which is based on a Markov random walk on the data, offers a general scheme of simultaneously reorganizing and subsampling graphs and arbitrarily shaped data sets in high dimensions using intrinsic geometry. We show that clustering in embedding spaces is equivalent to compressing operators. The objective of data partitioning and clustering is to coarsegrain the random walk on the data while at the same time preserving a diffusion operator for the intrinsic geometry or connectivity of the data set up to some accuracy. We show that the quantization distortion in diffusion space bounds the error of compression of the operator, thus giving a rigorous justification for kmeans clustering in diffusion space and a precise measure of the performance of general clustering algorithms.
Diffusion maps, spectral clustering and eigenfunctions of fokkerplanck operators
 in Advances in Neural Information Processing Systems 18
, 2005
"... This paper presents a diffusion based probabilistic interpretation of spectral clustering and dimensionality reduction algorithms that use the eigenvectors of the normalized graph Laplacian. Given the pairwise adjacency matrix of all points, we define a diffusion distance between any two data points ..."
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Cited by 65 (10 self)
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This paper presents a diffusion based probabilistic interpretation of spectral clustering and dimensionality reduction algorithms that use the eigenvectors of the normalized graph Laplacian. Given the pairwise adjacency matrix of all points, we define a diffusion distance between any two data points and show that the low dimensional representation of the data by the first few eigenvectors of the corresponding Markov matrix is optimal under a certain mean squared error criterion. Furthermore, assuming that data points are random samples from a density p(x) = e −U(x) we identify these eigenvectors as discrete approximations of eigenfunctions of a FokkerPlanck operator in a potential 2U(x) with reflecting boundary conditions. Finally, applying known results regarding the eigenvalues and eigenfunctions of the continuous FokkerPlanck operator, we provide a mathematical justification for the success of spectral clustering and dimensional reduction algorithms based on these first few eigenvectors. This analysis elucidates, in terms of the characteristics of diffusion processes, many empirical findings regarding spectral clustering algorithms.
A GromovHausdorff framework with diffusion geometry for topologicallyrobust nonrigid shape matching
 IMA PREPRINT SERIES# 2240
, 2009
"... ..."
Shape Google: a computer vision approach to invariant shape retrieval
 Proc. NORDIA
, 2009
"... Featurebased methods have recently gained popularity in computer vision and pattern recognition communities, in applications such as object recognition and image retrieval. In this paper, we explore analogous approaches in the 3D world applied to the problem of nonrigid shape search and retrieval ..."
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Cited by 39 (17 self)
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Featurebased methods have recently gained popularity in computer vision and pattern recognition communities, in applications such as object recognition and image retrieval. In this paper, we explore analogous approaches in the 3D world applied to the problem of nonrigid shape search and retrieval in large databases. 1.
Data Fusion and Multicue Data Matching by Diffusion Maps
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... Data fusion and multicue data matching are fundamental tasks of highdimensional data analysis. In this paper, we apply the recently introduced diffusion framework to address these tasks. Our contribution is threefold. First, we present the LaplaceBeltrami approach for computing density invariant ..."
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Cited by 38 (4 self)
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Data fusion and multicue data matching are fundamental tasks of highdimensional data analysis. In this paper, we apply the recently introduced diffusion framework to address these tasks. Our contribution is threefold. First, we present the LaplaceBeltrami approach for computing density invariant embeddings which are essential for integrating different sources of data. Second, we describe a refinement of the Nyström extension algorithm called “geometric harmonics”. We also explain how to use this tool for data assimilation. Finally, we introduce a multicue data matching scheme based on nonlinear spectral graphs alignment. The effectiveness of the presented schemes is validated by applying it to the problems of lipreading and image sequence alignment.
ShapeGoogle: geometric words and expressions for invariant shape retrieval
, 2010
"... The computer vision and pattern recognition communities have recently witnessed a surge of featurebased methods in object recognition and image retrieval applications. These methods allow representing images as collections of “visual words ” and treat them using text search approaches following the ..."
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Cited by 33 (5 self)
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The computer vision and pattern recognition communities have recently witnessed a surge of featurebased methods in object recognition and image retrieval applications. These methods allow representing images as collections of “visual words ” and treat them using text search approaches following the “bag of features ” paradigm. In this paper, we explore analogous approaches in the 3D world applied to the problem of nonrigid shape retrieval in large databases. Using multiscale diffusion heat kernels as “geometric words”, we construct compact and informative shape descriptors by means of the “bag of features ” approach. We also show that considering pairs of “geometric words ” (“geometric expressions”) allows creating spatiallysensitive bags of features with better discriminativity. Finally, adopting metric learning approaches, we show that shapes can be efficiently represented as binary codes. Our approach achieves stateoftheart results on the SHREC 2010 largescale shape retrieval benchmark.
Scaleinvariant heat kernel signatures for nonrigid shape recognition
 In Proc. CVPR
, 2010
"... One of the biggest challenges in nonrigid shape retrieval and comparison is the design of a shape descriptor that would maintain invariance under a wide class of transformations the shape can undergo. Recently, heat kernel signature was introduced as an intrinsic local shape descriptor based on dif ..."
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Cited by 28 (9 self)
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One of the biggest challenges in nonrigid shape retrieval and comparison is the design of a shape descriptor that would maintain invariance under a wide class of transformations the shape can undergo. Recently, heat kernel signature was introduced as an intrinsic local shape descriptor based on diffusion scalespace analysis. In this paper, we develop a scaleinvariant version of the heat kernel descriptor. Our construction is based on a logarithmically sampled scalespace in which shape scaling corresponds, up to a multiplicative constant, to a translation. This translation is undone using the magnitude of the Fourier transform. The proposed scaleinvariant local descriptors can be used in the bagoffeatures framework for shape retrieval in the presence of transformations such as isometric deformations, missing data, topological noise, and global and local scaling. We get significant performance improvement over stateoftheart algorithms on recently established nonrigid shape retrieval benchmarks. 1.
Graph laplacians and their convergence on random neighborhood graphs
 Journal of Machine Learning Research
, 2006
"... Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold. The graph Laplacian of such a graph is used in several machine learning methods like semisupervised learning, d ..."
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Cited by 27 (6 self)
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Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold. The graph Laplacian of such a graph is used in several machine learning methods like semisupervised learning, dimensionality reduction and clustering. In this paper we determine the pointwise limit of three different graph Laplacians used in the literature as the sample size increases and the neighborhood size approaches zero. We show that for a uniform measure on the submanifold all graph Laplacians have the same limit up to constants. However in the case of a nonuniform measure on the submanifold only the so called random walk graph Laplacian converges to the weighted LaplaceBeltrami operator.
Threedimensional Point Cloud Recognition via Distributions of Geometric Distances
, 2008
"... A geometric framework for the recognition of threedimensional objects represented by point clouds is introduced in this paper. The proposed approach is based on comparing distributions of intrinsic measurements on the point cloud. In particular, intrinsic distances are exploited as signatures for r ..."
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Cited by 27 (3 self)
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A geometric framework for the recognition of threedimensional objects represented by point clouds is introduced in this paper. The proposed approach is based on comparing distributions of intrinsic measurements on the point cloud. In particular, intrinsic distances are exploited as signatures for representing the point clouds. The first signature we introduce is the histogram of pairwise diffusion distances between all points on the shape surface. These distances represent the probability of traveling from one point to another in a fixed number of random steps, the average intrinsic distances of all possible paths of a given number of steps between the two points. This signature is augmented by the histogram of the actual pairwise geodesic distances in the point cloud, the distribution of the ratio between these two distances, as well as the distribution of the number of times each point lies on the shortest paths between other points. These signatures are not only geometric but also invariant to bends. We further augment these signatures by the distribution of a curvature function and the distribution of a curvature weighted distance. These
Regularization on graphs with functionadapted diffusion process
, 2006
"... Harmonic analysis and diffusion on discrete data has been shown to lead to stateoftheart algorithms for machine learning tasks, especially in the context of semisupervised and transductive learning. The success of these algorithms rests on the assumption that the function(s) to be studied (learn ..."
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Cited by 23 (5 self)
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Harmonic analysis and diffusion on discrete data has been shown to lead to stateoftheart algorithms for machine learning tasks, especially in the context of semisupervised and transductive learning. The success of these algorithms rests on the assumption that the function(s) to be studied (learned, interpolated, etc.) are smooth with respect to the geometry of the data. In this paper we present a method for modifying the given geometry so the function(s) to be studied are smoother with respect to the modified geometry, and thus more amenable to treatment using harmonic analysis methods. Among the many possible applications, we consider the problems of image denoising and transductive classification. In both settings, our approach improves on standard diffusion based methods.