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Image denoising by sparse 3D transformdomain collaborative filtering
 IEEE TRANS. IMAGE PROCESS
, 2007
"... We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call “groups.” Collaborative filtering is a special procedure d ..."
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Cited by 218 (29 self)
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We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call “groups.” Collaborative filtering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformation of a group, shrinkage of the transform spectrum, and inverse 3D transformation. The result is a 3D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to colorimage denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves stateoftheart denoising performance in terms of both peak signaltonoise ratio and subjective visual quality.
Self Organization of a Massive Document Collection
 IEEE Transactions on Neural Networks
"... This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the SelfOrganizing Map (SOM) algorithm. As the feature vectors for the documents we use statistical representations of their vocabularies. The m ..."
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Cited by 204 (14 self)
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This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the SelfOrganizing Map (SOM) algorithm. As the feature vectors for the documents we use statistical representations of their vocabularies. The main goal in our work has been to scale up the SOM algorithm to be able to deal with large amounts of highdimensional data. In a practical experiment we mapped 6,840,568 patent abstracts onto a 1,002,240node SOM. As the feature vectors we used 500dimensional vectors of stochastic figures obtained as random projections of weighted word histograms. Keywords Data mining, exploratory data analysis, knowledge discovery, large databases, parallel implementation, random projection, SelfOrganizing Map (SOM), textual documents. I. Introduction A. From simple searches to browsing of selforganized data collections Locating documents on the basis of keywords and simple search expressions is a c...
Exploring expression data: Identification and analysis of coexpressed genes
 Genome Research
, 1999
"... service ..."
Improved heterogeneous distance functions
 Journal of Artificial Intelligence Research
, 1997
"... Instancebased learning techniques typically handle continuous and linear input values well, but often do not handle nominal input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between nominal attribute values, but it largely ignores cont ..."
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Cited by 199 (10 self)
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Instancebased learning techniques typically handle continuous and linear input values well, but often do not handle nominal input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between nominal attribute values, but it largely ignores continuous attributes, requiring discretization to map continuous values into nominal values. This paper proposes three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference Metric (IVDM), and the Windowed Value Difference Metric (WVDM). These new distance functions are designed to handle applications with nominal attributes, continuous attributes, or both. In experiments on 48 applications the new distance metrics achieve higher classification accuracy on average than three previous distance functions on those datasets that have both nominal and continuous attributes.
Using mutual information for selecting features in supervised neural net learning
 IEEE Transactions on Neural Networks
, 1994
"... AbstractThis paper investigates the application of the mutual infor “ criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. Because the mutual information measures arbitrary dependencies between random variabl ..."
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Cited by 198 (1 self)
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AbstractThis paper investigates the application of the mutual infor “ criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. Because the mutual information measures arbitrary dependencies between random variables, it is suitable for assessing the “information content ” of features in complex classification tasks, where methods bases on linear relations (like the correlation) are prone to mistakes. The fact that the mutual information is independent of the coordinates chosen permits a robust estimation. Nonetheless, the use of the mutual information for tasks characterized by high input dimensionality requires suitable approximations because of the prohibitive demands on computation and samples. An algorithm is proposed that is based on a “greedy ” selection of the features and that takes both the mutual information with respect to the output class and with respect to the alreadyselected features into account. Finally the results of a series of experiments are discussed. Index TermsFeature extraction, neural network pruning, dimensionality reduction, mutual information, supervised learning,
On bias, variance, 0/1loss, and the curseofdimensionality
 Data Mining and Knowledge Discovery
, 1997
"... Abstract. The classification problem is considered in which an output variable y assumes discrete values with respective probabilities that depend upon the simultaneous values of a set of input variables x ={x1,...,xn}.At issue is how error in the estimates of these probabilities affects classificat ..."
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Cited by 193 (1 self)
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Abstract. The classification problem is considered in which an output variable y assumes discrete values with respective probabilities that depend upon the simultaneous values of a set of input variables x ={x1,...,xn}.At issue is how error in the estimates of these probabilities affects classification error when the estimates are used in a classification rule. These effects are seen to be somewhat counter intuitive in both their strength and nature. In particular the bias and variance components of the estimation error combine to influence classification in a very different way than with squared error on the probabilities themselves. Certain types of (very high) bias can be canceled by low variance to produce accurate classification. This can dramatically mitigate the effect of the bias associated with some simple estimators like “naive ” Bayes, and the bias induced by the curseofdimensionality on nearestneighbor procedures. This helps explain why such simple methods are often competitive with and sometimes superior to more sophisticated ones for classification, and why “bagging/aggregating ” classifiers can often improve accuracy. These results also suggest simple modifications to these procedures that can (sometimes dramatically) further improve their classification performance.
Anomaly Detection: A Survey
, 2007
"... Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and c ..."
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Cited by 186 (4 self)
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Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the di®erent directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
Coupled twoway clustering analysis of gene microarray data
 Cell Biology
, 2000
"... We present a novel coupled twoway clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally ..."
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Cited by 174 (8 self)
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We present a novel coupled twoway clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task: we present an algorithm, based on iterative clustering, which performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.
Emergence of Phase and ShiftInvariant Features by Decomposition of Natural Images into Independent Feature Subspaces
, 2000
"... this article, we show that the same principle of independence maximization can explain the emergence of phase and shiftinvariant features, similar to those found in complex cells. This new kind of emergence is obtained by maximizing the independence between norms of projections on linear subspaces ..."
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Cited by 169 (33 self)
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this article, we show that the same principle of independence maximization can explain the emergence of phase and shiftinvariant features, similar to those found in complex cells. This new kind of emergence is obtained by maximizing the independence between norms of projections on linear subspaces (instead of the independence of simple linear filter outputs). Thenorms of the projections on such "independent feature subspaces" then indicate the values of invariant features
A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirtythree Old and New Classification Algorithms
, 2000
"... . Twentytwo decision tree, nine statistical, and two neural network algorithms are compared on thirtytwo datasets in terms of classication accuracy, training time, and (in the case of trees) number of leaves. Classication accuracy is measured by mean error rate and mean rank of error rate. Both cr ..."
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Cited by 167 (7 self)
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. Twentytwo decision tree, nine statistical, and two neural network algorithms are compared on thirtytwo datasets in terms of classication accuracy, training time, and (in the case of trees) number of leaves. Classication accuracy is measured by mean error rate and mean rank of error rate. Both criteria place a statistical, splinebased, algorithm called Polyclass at the top, although it is not statistically signicantly dierent from twenty other algorithms. Another statistical algorithm, logistic regression, is second with respect to the two accuracy criteria. The most accurate decision tree algorithm is Quest with linear splits, which ranks fourth and fth, respectively. Although splinebased statistical algorithms tend to have good accuracy, they also require relatively long training times. Polyclass, for example, is third last in terms of median training time. It often requires hours of training compared to seconds for other algorithms. The Quest and logistic regression algor...