Results 1  10
of
260
Face recognition using laplacianfaces
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... Abstract—We propose an appearancebased face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) wh ..."
Abstract

Cited by 389 (38 self)
 Add to MetaCart
(Show Context)
Abstract—We propose an appearancebased face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition. Index Terms—Face recognition, principal component analysis, linear discriminant analysis, locality preserving projections, face manifold, subspace learning. 1
Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
 Proceedings of the National Academy of Sciences
, 2005
"... of contexts of data analysis, such as spectral graph theory, manifold learning, nonlinear principal components and kernel methods. We augment these approaches by showing that the diffusion distance is a key intrinsic geometric quantity linking spectral theory of the Markov process, Laplace operators ..."
Abstract

Cited by 257 (45 self)
 Add to MetaCart
of contexts of data analysis, such as spectral graph theory, manifold learning, nonlinear principal components and kernel methods. We augment these approaches by showing that the diffusion distance is a key intrinsic geometric quantity linking spectral theory of the Markov process, Laplace operators, or kernels, to the corresponding geometry and density of the data. This opens the door to the application of methods from numerical analysis and signal processing to the analysis of functions and transformations of the data. Abstract. We provide a framework for structural multiscale geometric organization of graphs and subsets of Rn. We use diffusion semigroups to generate multiscale geometries in order to organize and represent complex structures. We show that appropriately selected eigenfunctions or scaling functions of Markov matrices, which describe local transitions, lead to macroscopic descriptions at different scales. The process of iterating or diffusing the Markov matrix is seen as a generalization of some aspects of the Newtonian paradigm, in which local infinitesimal transitions of a system lead to global macroscopic descriptions by integration. In Part I below, we provide a unified view of ideas from data analysis, machine learning and numerical analysis. In Part II [1], we augment this approach by introducing fast orderN algorithms for homogenization of heterogeneous structures as well as for data representation. 1.
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 ..."
Abstract

Cited by 158 (5 self)
 Add to MetaCart
(Show Context)
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.
Sensor Positioning in Wireless Adhoc Sensor Networks Using Multidimensional Scaling
, 2004
"... Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. In the paper, we first study some situations where most existing sensor positioning methods tend to fail to perform well, an example being when the topology of a sensor network is anisotropic. Then, we ..."
Abstract

Cited by 147 (0 self)
 Add to MetaCart
Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. In the paper, we first study some situations where most existing sensor positioning methods tend to fail to perform well, an example being when the topology of a sensor network is anisotropic. Then, we explore the idea of using dimensionality reduction techniques to estimate sensors coordinates in two (or three) dimensional space, and we propose a distributed sensor positioning method based on multidimensional scaling technique to deal with these challenging conditions. Multidimensional scaling and coordinate alignment techniques are applied to recover positions of adjacent sensors. The estimated positions of the anchors are compared with their true physical positions and corrected, The positions of other sensors are corrected accordingly. With iterative adjustment, our method can overcome adverse network and terrain conditions, and generate accurate sensor position. We also propose an on demand sensor positioning method based on the above method.
Random projections of smooth manifolds
 Foundations of Computational Mathematics
, 2006
"... We propose a new approach for nonadaptive dimensionality reduction of manifoldmodeled data, demonstrating that a small number of random linear projections can preserve key information about a manifoldmodeled signal. We center our analysis on the effect of a random linear projection operator Φ: R N ..."
Abstract

Cited by 144 (26 self)
 Add to MetaCart
(Show Context)
We propose a new approach for nonadaptive dimensionality reduction of manifoldmodeled data, demonstrating that a small number of random linear projections can preserve key information about a manifoldmodeled signal. We center our analysis on the effect of a random linear projection operator Φ: R N → R M, M < N, on a smooth wellconditioned Kdimensional submanifold M ⊂ R N. As our main theoretical contribution, we establish a sufficient number M of random projections to guarantee that, with high probability, all pairwise Euclidean and geodesic distances between points on M are wellpreserved under the mapping Φ. Our results bear strong resemblance to the emerging theory of Compressed Sensing (CS), in which sparse signals can be recovered from small numbers of random linear measurements. As in CS, the random measurements we propose can be used to recover the original data in R N. Moreover, like the fundamental bound in CS, our requisite M is linear in the “information level” K and logarithmic in the ambient dimension N; we also identify a logarithmic dependence on the volume and conditioning of the manifold. In addition to recovering faithful approximations to manifoldmodeled signals, however, the random projections we propose can also be used to discern key properties about the manifold. We discuss connections and contrasts with existing techniques in manifold learning, a setting where dimensionality reducing mappings are typically nonlinear and constructed adaptively from a set of sampled training data.
Nonlinear dimensionality reduction by semidefinite programming and kernel matrix factorization
 in Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics
, 2005
"... We describe an algorithm for nonlinear dimensionality reduction based on semidefinite programming and kernel matrix factorization. The algorithm learns a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. In earlier work, the kernel matrix was learned by maximiz ..."
Abstract

Cited by 66 (5 self)
 Add to MetaCart
(Show Context)
We describe an algorithm for nonlinear dimensionality reduction based on semidefinite programming and kernel matrix factorization. The algorithm learns a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. In earlier work, the kernel matrix was learned by maximizing the variance in feature space while preserving the distances and angles between nearest neighbors. In this paper, adapting recent ideas from semisupervised learning on graphs, we show that the full kernel matrix can be very well approximated by a product of smaller matrices. Representing the kernel matrix in this way, we can reformulate the semidefinite program in terms of a much smaller submatrix of inner products between randomly chosen landmarks. The new framework leads to orderofmagnitude reductions in computation time and makes it possible to study much larger problems in manifold learning. 1
Data fusion and multicue data matching by diffusion maps
 IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Abstract—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 i ..."
Abstract

Cited by 57 (5 self)
 Add to MetaCart
(Show Context)
Abstract—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. Index Terms—Pattern matching, graph theory, graph algorithms, Markov processes, machine learning, data mining, image databases. Ç 1
Vector diffusion maps and the connection laplacian
 CComm. Pure Appl. Math
"... Abstract. We introduce vector diffusion maps (VDM), a new mathematical framework for organizing and analyzing massive high dimensional data sets, images and shapes. VDM is a mathematical and algorithmic generalization of diffusion maps and other nonlinear dimensionality reduction methods, such as L ..."
Abstract

Cited by 48 (13 self)
 Add to MetaCart
Abstract. We introduce vector diffusion maps (VDM), a new mathematical framework for organizing and analyzing massive high dimensional data sets, images and shapes. VDM is a mathematical and algorithmic generalization of diffusion maps and other nonlinear dimensionality reduction methods, such as LLE, ISOMAP and Laplacian eigenmaps. While existing methods are either directly or indirectly related to the heat kernel for functions over the data, VDM is based on the heat kernel for vector fields. VDM provides tools for organizing complex data sets, embedding them in a low dimensional space, and interpolating and regressing vector fields over the data. In particular, it equips the data with a metric, which we refer to as the vector diffusion distance. In the manifold learning setup, where the data set is distributed on (or near) a low dimensional manifold M d embedded in R p, we prove the relation between VDM and the connectionLaplacian operator for vector fields over the manifold. Key words. Dimensionality reduction, vector fields, heat kernel, parallel transport, local principal component analysis, alignment. 1. Introduction. Apopularwaytodescribethe
Convergence of laplacian eigenmaps
 In NIPS
, 2006
"... Geometrically based methods for various tasks of machine learning have attracted considerable attention over the last few years. In this paper we show convergence of eigenvectors of the point cloud Laplacian to the eigenfunctions of the LaplaceBeltrami operator on the underlying manifold, thus esta ..."
Abstract

Cited by 46 (4 self)
 Add to MetaCart
(Show Context)
Geometrically based methods for various tasks of machine learning have attracted considerable attention over the last few years. In this paper we show convergence of eigenvectors of the point cloud Laplacian to the eigenfunctions of the LaplaceBeltrami operator on the underlying manifold, thus establishing the first convergence results for a spectral dimensionality reduction algorithm in the manifold setting. 1
Riemannian manifold learning
 IEEE Trans. Pattern Anal. Mach. Intell
, 2008
"... Abstract—Recently, manifold learning has beenwidely exploited in pattern recognition, data analysis, andmachine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input highdimensional data lie on an intrinsically lowdimensi ..."
Abstract

Cited by 42 (0 self)
 Add to MetaCart
(Show Context)
Abstract—Recently, manifold learning has beenwidely exploited in pattern recognition, data analysis, andmachine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input highdimensional data lie on an intrinsically lowdimensional Riemannian manifold. The main idea is to formulate the dimensionality reduction problem as a classical problem in Riemannian geometry, that is, how to construct coordinate charts for a given Riemannian manifold? We implement the Riemannian normal coordinate chart, which has been the most widely used in Riemannian geometry, for a set of unorganized data points. First, two input parameters (the neighborhood size k and the intrinsic dimension d) are estimated based on an efficient simplicial reconstruction of the underlying manifold. Then, the normal coordinates are computed to map the input highdimensional data into a lowdimensional space. Experiments on synthetic data, as well as realworld images, demonstrate that our algorithm can learn intrinsic geometric structures of the data, preserve radial geodesic distances, and yield regular embeddings.