Journal ArticleDOI
Data Visualization by Multimensional Scaling: A Deterministic Annealing Approach
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TLDR
A novel deterministic annealing algorithm is presented for SSTRESS and Sammon mapping, derived in the framework of maximum entropy estimation, and the superiority of this optimization technique compared to conventional gradient descent methods is demonstrated.About:
This article is published in Pattern Recognition.The article was published on 1996-10-01. It has received 63 citations till now. The article focuses on the topics: Nonlinear dimensionality reduction & Sammon mapping.read more
Citations
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Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI
Think globally, fit locally: unsupervised learning of low dimensional manifolds
Lawrence K. Saul,Sam T. Roweis +1 more
TL;DR: Locally linear embedding (LLE), an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data, is described and several extensions that enhance its performance are discussed.
Journal ArticleDOI
A Survey on Multidimensional Scaling
TL;DR: This survey presents multidimensional scaling (MDS) methods and their applications in real world by explaining the basic notions of classical MDS and how MDS can be helpful to analyze the multid dimensional data.
Journal ArticleDOI
Fast communication: Gabor feature-based face recognition using supervised locality preserving projection
TL;DR: A novel Gabor-based supervised locality preserving projection (GSLPP) method for face recognition using class labels of data points to enhance its discriminant power in their mapping into a low-dimensional space is introduced.
Book ChapterDOI
Path Based Pairwise Data Clustering with Application to Texture Segmentation
TL;DR: A pairwise clustering cost function with a novel dissimilarity measure emphasizing connectedness in feature space rather than compactness is proposed and applied to segment textured images with strong texture gradients based on dissimilarities between image patches.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.