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6,212
An Analysis of Learned Proximity Functions
"... ABSTRACT A lot of recent work has shown that the proximity of terms can be exploited to improve the performance of information retrieval systems. We review a recent approach that uses an intuitive framework to incorporate proximity functions into vector based information retrieval systems. More imp ..."
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ABSTRACT A lot of recent work has shown that the proximity of terms can be exploited to improve the performance of information retrieval systems. We review a recent approach that uses an intuitive framework to incorporate proximity functions into vector based information retrieval systems. More
The Growth of the Nevanlinna Proximity Function
, 2009
"... Abstract. Let f be a meromorphic mapping from C n into a compact complex manifold M . In this paper we give some estimates of the growth of the proximity function ..."
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Abstract. Let f be a meromorphic mapping from C n into a compact complex manifold M . In this paper we give some estimates of the growth of the proximity function
SIGNAL RECOVERY BY PROXIMAL FORWARD-BACKWARD SPLITTING
- MULTISCALE MODEL. SIMUL. TO APPEAR
"... We show that various inverse problems in signal recovery can be formulated as the generic problem of minimizing the sum of two convex functions with certain regularity properties. This formulation makes it possible to derive existence, uniqueness, characterization, and stability results in a unifi ..."
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Cited by 509 (24 self)
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We show that various inverse problems in signal recovery can be formulated as the generic problem of minimizing the sum of two convex functions with certain regularity properties. This formulation makes it possible to derive existence, uniqueness, characterization, and stability results in a
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
, 1997
"... A new access meth d, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
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Cited by 663 (38 self)
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A new access meth d, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion
Features of similarity.
- Psychological Review
, 1977
"... Similarity plays a fundamental role in theories of knowledge and behavior. It serves as an organizing principle by which individuals classify objects, form concepts, and make generalizations. Indeed, the concept of similarity is ubiquitous in psychological theory. It underlies the accounts of stimu ..."
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Cited by 1455 (2 self)
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. These models represent objects as points in some coordinate space such that the observed dissimilarities between objects correspond to the metric distances between the respective points. Practically all analyses of proximity data have been metric in nature, although some (e.g., hierarchical clustering) yield
Handling Churn in a DHT
- In Proceedings of the USENIX Annual Technical Conference
, 2004
"... This paper addresses the problem of churn---the continuous process of node arrival and departure---in distributed hash tables (DHTs). We argue that DHTs should perform lookups quickly and consistently under churn rates at least as high as those observed in deployed P2P systems such as Kazaa. We then ..."
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Cited by 450 (22 self)
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then show through experiments on an emulated network that current DHT implementations cannot handle such churn rates. Next, we identify and explore three factors affecting DHT performance under churn: reactive versus periodic failure recovery, message timeout calculation, and proximity neighbor selection
A generalization of proximity functions for K-means
- 2007 Seventh IEEE International Conference on Data Mining
"... K-means is a widely used partitional clustering method. A large amount of effort has been made on finding better proximity (distance) functions for K-means. However, the common characteristics of proximity functions remain un-known. To this end, in this paper, we show that all proxim-ity functions t ..."
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Cited by 4 (2 self)
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K-means is a widely used partitional clustering method. A large amount of effort has been made on finding better proximity (distance) functions for K-means. However, the common characteristics of proximity functions remain un-known. To this end, in this paper, we show that all proxim-ity functions
Proximal Splitting Methods in Signal Processing
"... The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set. This tool, which plays a central role in the analysis and the numerical solution of convex optimization problems, has recently been introduced in the arena of inverse problems ..."
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Cited by 266 (31 self)
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The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set. This tool, which plays a central role in the analysis and the numerical solution of convex optimization problems, has recently been introduced in the arena of inverse
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
, 2010
"... Stochastic subgradient methods are widely used, well analyzed, and constitute effective tools for optimization and online learning. Stochastic gradient methods ’ popularity and appeal are largely due to their simplicity, as they largely follow predetermined procedural schemes. However, most common s ..."
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Cited by 311 (3 self)
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, in essence, allows us to find needles in haystacks in the form of very predictive but rarely seenfeatures. Ourparadigmstemsfromrecentadvancesinstochasticoptimizationandonlinelearning which employ proximal functions to control the gradient steps of the algorithm. We describe and analyze an apparatus
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