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111
Statistical pattern recognition: A review
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2000
"... The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques ..."
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Cited by 657 (22 self)
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The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have bean receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the wellknown methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
A Minimum Description Length Approach to Statistical Shape Modelling
 IEEE Transactions on Medical Imaging
, 2001
"... We describe a method for automatically building statistical shape models from a training set of exam ple boundaries / surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of ..."
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Cited by 177 (11 self)
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We describe a method for automatically building statistical shape models from a training set of exam ple boundaries / surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences between all members of a set of training shapes. Often this is achieved by locating a set of qandmarks manually on each training image, which is timeconsuming and subjective in 2D, and almost impossible in 3D. We describe how shape models can be built automatically by posing the correspondence problem as one of finding the parameterization for each shape in the training set. We select the set of parameterizations that build the best model. We define best as that which min imizes the description length of the training set, arguing that this leads to models with good compactness, specificity and generalization ability. We show how a set of shape parameterizations can be represented and manipulated in order to build a minimum description length model. Results are given for several different training sets of 2D boundaries, showing that the proposed method constructs better models than other approaches including manual landmarking  the current gold standard. We also show that the method can be extended straightforwardly to 3D.
Data Clustering: 50 Years Beyond KMeans
, 2008
"... Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and m ..."
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Cited by 75 (3 self)
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Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is exploratory in nature to find structure in data. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, Kmeans, was first published in 1955. In spite of the fact that Kmeans was proposed over 50 years ago and thousands of clustering algorithms have been published since then, Kmeans is still widely used. This speaks to the difficulty of designing a general purpose clustering algorithm and the illposed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semisupervised clustering, ensemble clustering, simultaneous feature selection, and data clustering and large scale data clustering.
Segmentation of multivariate mixed data via lossy coding and compression
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2007
"... Abstract—In this paper, based on ideas from lossy data coding and compression, we present a simple but effective technique for segmenting multivariate mixed data that are drawn from a mixture of Gaussian distributions, which are allowed to be almost degenerate. The goal is to find the optimal segmen ..."
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Cited by 68 (13 self)
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Abstract—In this paper, based on ideas from lossy data coding and compression, we present a simple but effective technique for segmenting multivariate mixed data that are drawn from a mixture of Gaussian distributions, which are allowed to be almost degenerate. The goal is to find the optimal segmentation that minimizes the overall coding length of the segmented data, subject to a given distortion. By analyzing the coding length/rate of mixed data, we formally establish some strong connections of data segmentation to many fundamental concepts in lossy data compression and ratedistortion theory. We show that a deterministic segmentation is approximately the (asymptotically) optimal solution for compressing mixed data. We propose a very simple and effective algorithm that depends on a single parameter, the allowable distortion. At any given distortion, the algorithm automatically determines the corresponding number and dimension of the groups and does not involve any parameter estimation. Simulation results reveal intriguing phasetransitionlike behaviors of the number of segments when changing the level of distortion or the amount of outliers. Finally, we demonstrate how this technique can be readily applied to segment real imagery and bioinformatic data. Index Terms—Multivariate mixed data, data segmentation, data clustering, rate distortion, lossy coding, lossy compression, image segmentation, microarray data clustering. 1
A tutorial introduction to the minimum description length principle
 in Advances in Minimum Description Length: Theory and Applications. 2005
"... ..."
Bootstraps for Time Series
, 1999
"... We compare and review block, sieve and local bootstraps for time series and thereby illuminate theoretical facts as well as performance on nitesample data. Our (re) view is selective with the intention to get a new and fair picture about some particular aspects of bootstrapping time series. The ge ..."
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Cited by 56 (4 self)
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We compare and review block, sieve and local bootstraps for time series and thereby illuminate theoretical facts as well as performance on nitesample data. Our (re) view is selective with the intention to get a new and fair picture about some particular aspects of bootstrapping time series. The generality of the block bootstrap is contrasted by sieve bootstraps. We discuss implementational dis/advantages and argue that two types of sieves outperform the block method, each of them in its own important niche, namely linear and categorical processes, respectively. Local bootstraps, designed for nonparametric smoothing problems, are easy to use and implement but exhibit in some cases low performance. Key words and phrases. Autoregression, block bootstrap, categorical time series, context algorithm, double bootstrap, linear process, local bootstrap, Markov chain, sieve bootstrap, stationary process. 1 Introduction Bootstrapping can be viewed as simulating a statistic or statistical pro...
3D Statistical Shape Models Using Direct Optimisation of Description Length
, 2002
"... We describea n a26`('9b method for buildingoptima 3D sta22j9b'2 sha e models from sets oftraj'Hj sha es. Althoughsha e models showconsideraj promisea a bami for segmentingan interpreting imainga ma jordra wba k of theae9`2j h is the need toestaH69 a dense correspondenceadenc a trance9 set ofexa') ..."
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Cited by 54 (4 self)
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We describea n a26`('9b method for buildingoptima 3D sta22j9b'2 sha e models from sets oftraj'Hj sha es. Althoughsha e models showconsideraj promisea a bami for segmentingan interpreting imainga ma jordra wba k of theae9`2j h is the need toestaH69 a dense correspondenceadenc a trance9 set ofexa')( sha es. It is importa t to esta)`9b the correct correspondence, otherwise poor models ca result. In 2D, thisca be a hieved usingma ua `la9`'H`9b but in 3D this becomesimpra2`269 We show it is possible toesta6jH9 correspondences automatically, byca6)22 the correspondence problema one of finding the`optima) paima)9b`2'2)9 of ea hsha e in thetra'22 set. We describea n explicit representares ofsurfa6 paa6(9b`j"`9a tha ensures the resulting correspondencesad legac ag show how this representaen9ca bemaH('9b2)j to minimise thed933J292 length of the tra'H22 set using the model. This results incompaH models with good generab2('H9 properties. Resultsas reported for two sets ofbiomedica sha es, showingsignifica t improvement in model propertiescompa9' to thoseobta9j) usinga uniform surfam paam92))559b2'6 1
MDL Denoising
 IEEE Transactions on Information Theory
, 1999
"... The socalled denoising problem, relative to normal models for noise, is formalized such that `noise' is defined as the incompressible part in the data while the compressible part defines the meaningful information bearing signal. Such a decomposition is effected by minimization of the ideal code ..."
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Cited by 49 (9 self)
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The socalled denoising problem, relative to normal models for noise, is formalized such that `noise' is defined as the incompressible part in the data while the compressible part defines the meaningful information bearing signal. Such a decomposition is effected by minimization of the ideal code length, called for by the Minimum Description Length (MDL) principle, and obtained by an application of the normalized maximum likelihood technique to the primary parameters, their range, and their number. For any orthonormal regression matrix, such as defined by wavelet transforms, the minimization can be done with a threshold for the squared coefficients resulting from the expansion of the data sequence in the basis vectors defined by the matrix. keywords: linear regression, wavelet transforms, threshold, stochastic complexity, Kolmogorov sufficient statistics 1 Introduction Intuitively speaking the socalled `denoising' problem is to separate an observed data sequence x 1 ; x 2 ; ...
Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms
, 2003
"... Many clustering and segmentation algorithms both suffer from the limitation that the number of clusters/segments are specified by a human user. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. In this pape ..."
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Cited by 48 (2 self)
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Many clustering and segmentation algorithms both suffer from the limitation that the number of clusters/segments are specified by a human user. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. In this paper, we investigate techniques to determine the number of clusters or segments to return from hierarchical clustering and segmentation algorithms. We propose an efficient algorithm, the L method, that finds the “knee ” in a ‘ # of clusters vs. clustering evaluation metric ’ graph. Using the knee is wellknown, but is not a particularly wellunderstood method to determine the number of clusters. We explore the feasibility of this method, and attempt to determine in which situations it will and will not work. We also compare the L method to existing methods based on the accuracy of the number of clusters that are determined and efficiency. Our results show favorable performance for these criteria compared to the existing methods that were evaluated.