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430
An Eigenspace Update Algorithm for Image Analysis
, 1997
"... this paper However, the vision research community has largely overlooked makes the following contributions: parallel developments in signal processing and numerical linear algebra concerning efficient eigenspace updating algorithms. . We provide a comparison of some of the popular tech These new ..."
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Cited by 114 (3 self)
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this paper However, the vision research community has largely overlooked makes the following contributions: parallel developments in signal processing and numerical linear algebra concerning efficient eigenspace updating algorithms. . We provide a comparison of some of the popular tech These new developments are significant for two reasons: Adopt niques existing in the vision literature for SVD/KLT com ing them will make some of the current vision algorithms more putations and point out the problems associated with robust and efficient. More important is the fact that incremental those techniques
Contentbased Organization and Visualization of Music Archives
, 2002
"... With Islands of Music we present a system which facilitates exploration of music libraries without requiring manual genre classification. Given pieces of music in raw audio format we estimate their perceived sound similarities based on psychoacoustic models. Subsequently, the pieces are organized on ..."
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Cited by 102 (24 self)
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With Islands of Music we present a system which facilitates exploration of music libraries without requiring manual genre classification. Given pieces of music in raw audio format we estimate their perceived sound similarities based on psychoacoustic models. Subsequently, the pieces are organized on a 2dimensional map so that similar pieces are located close to each other. A visualization using a metaphor of geographic maps provides an intuitive interface where islands resemble genres or styles of music. We demonstrate the approach using a collection of 359 pieces of music.
Neural Networks and Statistical Models
, 1994
"... There has been much publicity about the ability of artificial neural networks to learn and generalize. In fact, the most commonly used artificial neural networks, called multilayer perceptrons, are nothing more than nonlinear regression and discriminant models that can be implemented with standard s ..."
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Cited by 99 (1 self)
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There has been much publicity about the ability of artificial neural networks to learn and generalize. In fact, the most commonly used artificial neural networks, called multilayer perceptrons, are nothing more than nonlinear regression and discriminant models that can be implemented with standard statistical software. This paper explains what neural networks are, translates neural network jargon into statistical jargon, and shows the relationships between neural networks and statistical models such as generalized linear models, maximum redundancy analysis, projection pursuit, and cluster analysis.
Data Exploration Using SelfOrganizing Maps
 ACTA POLYTECHNICA SCANDINAVICA: MATHEMATICS, COMPUTING AND MANAGEMENT IN ENGINEERING SERIES NO. 82
, 1997
"... Finding structures in vast multidimensional data sets, be they measurement data, statistics, or textual documents, is difficult and timeconsuming. Interesting, novel relations between the data items may be hidden in the data. The selforganizing map (SOM) algorithm of Kohonen can be used to aid the ..."
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Cited by 96 (4 self)
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Finding structures in vast multidimensional data sets, be they measurement data, statistics, or textual documents, is difficult and timeconsuming. Interesting, novel relations between the data items may be hidden in the data. The selforganizing map (SOM) algorithm of Kohonen can be used to aid the exploration: the structures in the data sets can be illustrated on special map displays. In this work, the methodology of using SOMs for exploratory data analysis or data mining is reviewed and developed further. The properties of the maps are compared with the properties of related methods intended for visualizing highdimensional multivariate data sets. In a set of case studies the SOM algorithm is applied to analyzing electroencephalograms, to illustrating structures of the standard of living in the world, and to organizing fulltext document collections. Measures are proposed for evaluating the quality of different types of maps in representing a given data set, and for measuring the robu...
A ContextSensitive Generalization of ICA
, 1996
"... Source separation arises in a surprising number of signal processing applications, from speech recognition to EEG analysis. In the square linear blind source separation problem without time delays, one must find an unmixing matrix which can detangle the result of mixing n unknown independent sources ..."
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Cited by 95 (7 self)
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Source separation arises in a surprising number of signal processing applications, from speech recognition to EEG analysis. In the square linear blind source separation problem without time delays, one must find an unmixing matrix which can detangle the result of mixing n unknown independent sources through an unknown n \Theta n mixing matrix. The recently introduced ICA blind source separation algorithm (Baram and Roth 1994; Bell and Sejnowski 1995) is a powerful and surprisingly simple technique for solving this problem. ICA is all the more remarkable for performing so well despite making absolutely no use of the temporal structure of its input! This paper presents a new algorithm, contextual ICA, which derives from a maximum likelihood density estimation formulation of the problem. cICA can incorporate arbitrarily complex adaptive historysensitive source models, and thereby make use of the temporal structure of its input. This allows it to separate in a number of situations where s...
Highdimensional data analysis: The curses and blessings of dimensionality. AideMemoire of a Lecture at
 AMS Conference on Math Challenges of the 21st Century
, 2000
"... The coming century is surely the century of data. A combination of blind faith and serious purpose makes our society invest massively in the collection and processing of data of all kinds, on scales unimaginable until recently. Hyperspectral Imagery, Internet Portals, Financial tickbytick data, an ..."
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Cited by 89 (0 self)
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The coming century is surely the century of data. A combination of blind faith and serious purpose makes our society invest massively in the collection and processing of data of all kinds, on scales unimaginable until recently. Hyperspectral Imagery, Internet Portals, Financial tickbytick data, and DNA Microarrays are just a few of the betterknown sources, feeding data in torrential streams into scientific and business databases worldwide. In traditional statistical data analysis, we think of observations of instances of particular phenomena (e.g. instance ↔ human being), these observations being a vector of values we measured on several variables (e.g. blood pressure, weight, height,...). In traditional statistical methodology, we assumed many observations and a few, wellchosen variables. The trend today is towards more observations but even more so, to radically larger numbers of variables – voracious, automatic, systematic collection of hyperinformative detail about each observed instance. We are seeing examples where the observations gathered on individual instances are curves, or spectra, or images, or
On the Early History of the Singular Value Decomposition
, 1992
"... This paper surveys the contributions of five mathematicians  Eugenio Beltrami (18351899), Camille Jordan (18381921), James Joseph Sylvester (18141897), Erhard Schmidt (18761959), and Hermann Weyl (18851955)  who were responsible for establishing the existence of the singular value de ..."
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Cited by 82 (1 self)
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This paper surveys the contributions of five mathematicians  Eugenio Beltrami (18351899), Camille Jordan (18381921), James Joseph Sylvester (18141897), Erhard Schmidt (18761959), and Hermann Weyl (18851955)  who were responsible for establishing the existence of the singular value decomposition and developing its theory.
Properties of embedding methods for similarity searching in metric spaces
 PAMI
, 2003
"... Complex data types—such as images, documents, DNA sequences, etc.—are becoming increasingly important in modern database applications. A typical query in many of these applications seeks to find objects that are similar to some target object, where (dis)similarity is defined by some distance functi ..."
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Cited by 80 (4 self)
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Complex data types—such as images, documents, DNA sequences, etc.—are becoming increasingly important in modern database applications. A typical query in many of these applications seeks to find objects that are similar to some target object, where (dis)similarity is defined by some distance function. Often, the cost of evaluating the distance between two objects is very high. Thus, the number of distance evaluations should be kept at a minimum, while (ideally) maintaining the quality of the result. One way to approach this goal is to embed the data objects in a vector space so that the distances of the embedded objects approximates the actual distances. Thus, queries can be performed (for the most part) on the embedded objects. In this paper, we are especially interested in examining the issue of whether or not the embedding methods will ensure that no relevant objects are left out (i.e., there are no false dismissals and, hence, the correct result is reported). Particular attention is paid to the SparseMap, FastMap, and MetricMap embedding methods. SparseMap is a variant of Lipschitz embeddings, while FastMap and MetricMap are inspired by dimension reduction methods for Euclidean spaces (using KLT or the related PCA and SVD). We show that, in general, none of these embedding methods guarantee that queries on the embedded objects have no false dismissals, while also demonstrating the limited cases in which the guarantee does hold. Moreover, we describe a variant of SparseMap that allows queries with no false dismissals. In addition, we show that with FastMap and MetricMap, the distances of the embedded objects can be much greater than the actual distances. This makes it impossible (or at least impractical) to modify FastMap and MetricMap to guarantee no false dismissals.
Wavelet base rate scalable video compression
 116 version 2  7
, 1999
"... Abstract—In this paper, we present a new wavelet based rate scalable video compression algorithm. We will refer to this new technique as the Scalable Adaptive Motion Compensated Wavelet (SAMCoW) ) algorithm. SAMCoW uses motion compensation to reduce temporal redundancy. The prediction error frames a ..."
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Cited by 65 (15 self)
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Abstract—In this paper, we present a new wavelet based rate scalable video compression algorithm. We will refer to this new technique as the Scalable Adaptive Motion Compensated Wavelet (SAMCoW) ) algorithm. SAMCoW uses motion compensation to reduce temporal redundancy. The prediction error frames and the intracoded frames are encoded using an approach similar to the embedded zerotree wavelet (EZW) coder. An adaptive motion compensation (AMC) scheme is described to address error propagation problems. We show that, using our AMC scheme, the quality of the decoded video can be maintained at various data rates. We also describe an EZW approach that exploits the interdependency between color components in the luminance/chrominance color space. We show that, in addition to providing a wide range of rate scalability, our encoder achieves comparable performance to the more traditional hybrid video coders, such as MPEG1 and H.263. Furthermore, our coding scheme allows the data rate to be dynamically changed during decoding, which is very appealing for networkoriented applications. Index Terms—Motion compensation, rate scalable, video compression, wavelet transform. I.
Theoretical Foundations of Transform Coding
, 2001
"... This article explains the fundamental principles of transform coding; these principles apply equally well to images, audio, video, and various other types of data, so abstract formulations are given. Much of the material presented here is adapted from [14, Chap. 2, 4]. The details on wavelet transfo ..."
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Cited by 65 (6 self)
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This article explains the fundamental principles of transform coding; these principles apply equally well to images, audio, video, and various other types of data, so abstract formulations are given. Much of the material presented here is adapted from [14, Chap. 2, 4]. The details on wavelet transformbased image compression and the JPEG2000 image compression standard are given in the following two articles of this special issue [38], [37]