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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 ..."
Abstract
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Cited by 1455 (2 self)
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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
An Information-Theoretic Definition of Similarity
- In Proceedings of the 15th International Conference on Machine Learning
, 1998
"... Similarity is an important and widely used concept. Previous definitions of similarity are tied to a particular application or a form of knowledge representation. We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model. We demonstrate ..."
Abstract
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Cited by 1243 (0 self)
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how our definition can be used to measure the similarity in a number of different domains.
Similarity of Color Images
, 1995
"... We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L 1 -, L 2 -, or L1 -distance between two cumulative color histograms can be used to define a similarity mea ..."
Abstract
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Cited by 495 (2 self)
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of storing the complete color distributions, the index contains only their dominant features. We implement this approach by storing the first three moments of each color channel of an image in the index, i.e., for a HSV image we store only 9 floating point numbers per image. The similarity function which
Efficient similarity search in sequence databases
, 1994
"... We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the first few frequencies are strong. Anot ..."
Abstract
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Cited by 515 (19 self)
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We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the first few frequencies are strong
Similarity search in high dimensions via hashing
, 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
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Cited by 641 (10 self)
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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
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 ..."
Abstract
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Cited by 663 (38 self)
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of objects and split management, whF h keep th M-tree always balanced - severalheralvFV split alternatives are considered and experimentally evaluated. Algorithd for similarity (range and k-nearest neigh bors) queries are also described. Results from extensive experimentationwith a prototype system
Self-Similarity Through High-Variability: Statistical Analysis of Ethernet LAN Traffic at the Source Level
- IEEE/ACM TRANSACTIONS ON NETWORKING
, 1997
"... A number of recent empirical studies of traffic measurements from a variety of working packet networks have convincingly demonstrated that actual network traffic is self-similar or long-range dependent in nature (i.e., bursty over a wide range of time scales) -- in sharp contrast to commonly made tr ..."
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Cited by 743 (24 self)
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A number of recent empirical studies of traffic measurements from a variety of working packet networks have convincingly demonstrated that actual network traffic is self-similar or long-range dependent in nature (i.e., bursty over a wide range of time scales) -- in sharp contrast to commonly made
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
, 2002
"... Matching elements of two data schemas or two data instances plays a key role in data warehousing, e-business, or even biochemical applications. In this paper we present a matching algorithm based on a fixpoint computation that is usable across different scenarios. The algorithm takes two graphs (sch ..."
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Cited by 592 (12 self)
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matter of fact, we evaluate the ‘accuracy ’ of the algorithm by counting the number of needed adjustments. We conducted a user study, in which our accuracy metric was used to estimate the labor savings that the users could obtain by utilizing our algorithm to obtain an initial matching. Finally, we
A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge
- PSYCHOLOGICAL REVIEW
, 1997
"... How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LS ..."
Abstract
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Cited by 1816 (10 self)
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rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other
Regularization and variable selection via the Elastic Net.
- J. R. Stat. Soc. Ser. B
, 2005
"... Abstract We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, wher ..."
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Cited by 973 (11 self)
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Abstract We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect
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