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Similarity search in high dimensions via hashing

by Aristides Gionis, Piotr Indyk, Rajeev Motwani , 1999
"... The nearest- or near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over high-dimensional data, e.g., image dat ..."
Abstract - Cited by 641 (10 self) - Add to MetaCart
to 20, searching in k-d trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than brute-force linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather

The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries

by Norio Katayama, et al. , 1997
"... Recently, similarity queries on feature vectors have been widely used to perform content-based retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SS-tree had been proposed for ..."
Abstract - Cited by 438 (3 self) - Add to MetaCart
Recently, similarity queries on feature vectors have been widely used to perform content-based retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SS-tree had been proposed

The Determinants of Credit Spread Changes.

by Pierre Collin-Dufresne , Robert S Goldstein , J Spencer Martin , Gurdip Bakshi , Greg Bauer , Dave Brown , Francesca Carrieri , Peter Christoffersen , Susan Christoffersen , Greg Duffee , Darrell Duffie , Vihang Errunza , Gifford Fong , Mike Gallmeyer , Laurent Gauthier , Rick Green , John Griffin , Jean Helwege , Kris Jacobs , Chris Jones , Andrew Karolyi , Dilip Madan , David Mauer , Erwan Morellec , Federico Nardari , N R Prabhala , Tony Sanders , Sergei Sarkissian , Bill Schwert , Ken Singleton , Chester Spatt , René Stulz - Journal of Finance , 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
Abstract - Cited by 422 (2 self) - Add to MetaCart
The contingent-claims approach implies that the debt claim has features similar to a short position in a put option. Since option values increase with volatility, it follows that this model predicts credit spreads should increase with volatility. This prediction is intuitive: increased volatility increases

Concept Decompositions for Large Sparse Text Data using Clustering

by Inderjit S. Dhillon, Dharmendra S. Modha - Machine Learning , 2000
"... . Unlabeled document collections are becoming increasingly common and available; mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as high-dimensional and sparse vectors--a few thousand dimensions and a sparsity of 95 to 99 ..."
Abstract - Cited by 407 (27 self) - Add to MetaCart
. Unlabeled document collections are becoming increasingly common and available; mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as high-dimensional and sparse vectors--a few thousand dimensions and a sparsity of 95

The adaptive nature of human categorization

by John R. Anderson - Psychological Review , 1991
"... A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partiti ..."
Abstract - Cited by 344 (2 self) - Add to MetaCart
of linearly nonseparable categories, effects of category labels, extraction of basic level categories, base-rate effects, probability matching in categorization, and trial-by-trial learning functions. Al-though the rational model considers just I level of categorization, it is shown how predictions can

Protein structure prediction and structural genomics

by David Baker, Andrej Sali - Science , 2001
"... Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information abou ..."
Abstract - Cited by 332 (14 self) - Add to MetaCart
Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information

Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition

by Chengjun Liu, Harry Wechsler - IEEE Trans. Image Processing , 2002
"... This paper introduces a novel Gabor-Fisher Classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the Enhanced Fisher linear discriminant Model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet represen ..."
Abstract - Cited by 286 (10 self) - Add to MetaCart
This paper introduces a novel Gabor-Fisher Classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the Enhanced Fisher linear discriminant Model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet

Leave-One-Out Support Vector Machines

by Jason Weston , 1999
"... We present a new learning algorithm for pattern recognition inspired by a recent upper bound on leave--one--out error [ Jaakkola and Haussler, 1999 ] proved for Support Vector Machines (SVMs) [ Vapnik, 1995; 1998 ] . The new approach directly minimizes the expression given by the bound in an attempt ..."
Abstract - Cited by 301 (5 self) - Add to MetaCart
in an attempt to minimize leave--one--out error. This gives a convex optimization problem which constructs a sparse linear classifier in feature space using the kernel technique. As such the algorithm possesses many of the same properties as SVMs. The main novelty of the algorithm is that apart from the choice

A Paraperspective Factorization Method for Shape and Motion Recovery

by Conrad J. Poelman, Takeo Kanade , 1997
"... The factorization method, first developed by Tomasi and Kanade, recovers both the shape of an object and its motion from a sequence of images, using many images and tracking many feature points to obtain highly redundant feature position information. The method robustly processes the feature traject ..."
Abstract - Cited by 292 (13 self) - Add to MetaCart
The factorization method, first developed by Tomasi and Kanade, recovers both the shape of an object and its motion from a sequence of images, using many images and tracking many feature points to obtain highly redundant feature position information. The method robustly processes the feature

The Space of Human Body Shapes: Reconstruction And Parameterization from Range Scans

by Brett Allen, Brian Curless, Zoran Popović - ACM TRANS. GRAPH , 2003
"... We develop a novel method for fitting high-resolution template meshes to detailed human body range scans with sparse 3D markers. We formulate an optimization problem in which the degrees of freedom are an affine transformation at each template vertex. The objective function is a weighted combination ..."
Abstract - Cited by 290 (4 self) - Add to MetaCart
combination of three measures: proximity of transformed vertices to the range data, similarity between neighboring transformations, and proximity of sparse markers at corresponding locations on the template and target surface. We solve for the transformations with a non-linear optimizer, run at two
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