The Experts below are selected from a list of 77700 Experts worldwide ranked by ideXlab platform
Jaideep Vaidya - One of the best experts on this subject based on the ideXlab platform.
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ICDE - Boolean Matrix Decomposition Problem: Theory, Variations and Applications to Data Engineering
2012 IEEE 28th International Conference on Data Engineering, 2012Co-Authors: Jaideep VaidyaAbstract:With the ubiquitous nature and sheer scale of data collection, the problem of data summarization is most critical for effective data management. Classical Matrix Decomposition techniques have often been used for this purpose, and have been the subject of much study. In recent years, several other forms of Decomposition, including Boolean Matrix Decomposition have become of significant practical interest. Since much of the data collected is categorical in nature, it can be viewed in terms of a Boolean Matrix. Boolean Matrix Decomposition (BMD), wherein a boolean Matrix is expressed as a product of two Boolean matrices, can be used to provide concise and interpretable representations of Boolean data sets. The decomposed matrices give the set of meaningful concepts and their combination which can be used to reconstruct the original data. Such Decompositions are useful in a number of application domains including role engineering, text mining as well as knowledge discovery from databases. In this seminar, we look at the theory underlying the BMD problem, study some of its variants and solutions, and examine different practical applications.
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ICDM - Extended Boolean Matrix Decomposition
2009 Ninth IEEE International Conference on Data Mining, 2009Co-Authors: Jaideep Vaidya, Vijayalakshmi Atluri, Yuan HongAbstract:With the vast increase in collection and storage of data, the problem of data summarization is most critical for effective data management. Since much of this data is categorical in nature, it can be viewed in terms of a Boolean Matrix. Boolean Matrix Decomposition (BMD) has been used to provide concise and interpretable representations of Boolean data sets. A Boolean Matrix can be expressed as a product of two Boolean matrices, where the first Matrix represents a set of meaningful concepts, and the second describes how the observed data can be expressed as combinations of those concepts. Typically, the combination is only in terms of the set union. In other words, a successful Boolean Matrix Decomposition gives a set of concepts and shows how every column of the input data can be expressed as a union of some subset of those concepts. However, this way of modeling only incompletely represents real data semantics. Essentially, it ignores a critical component -- the set difference operation: a column can be expressed as the combination of union of certain concepts as well as the exclusion of other concepts. This has two significant benefits. First, the total number of concepts required to describe the data may itself be reduced. Second, a more succinct summarization may be found for every column. In this paper, we propose the extended Boolean Matrix Decomposition (EBMD) problem, which aims to factor Boolean matrices using both the set union and set difference operations. We study several variants of the problem, show that they are NP-hard, and propose efficient heuristics to solve them. Extensive experimental results demonstrate the power of EBMD.
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optimal boolean Matrix Decomposition application to role engineering
International Conference on Data Engineering, 2008Co-Authors: Jaideep Vaidya, Vijayalakshmi AtluriAbstract:A Decomposition of a binary Matrix into two matrices gives a set of basis vectors and their appropriate combination to form the original Matrix. Such Decomposition solutions are useful in a number of application domains including text mining, role engineering as well as knowledge discovery. While a binary Matrix can be decomposed in several ways, however, certain Decompositions better characterize the semantics associated with the original Matrix in a succinct but comprehensive way. Indeed, one can find different Decompositions optimizing different criteria matching various semantics. In this paper, we first present a number of variants to the optimal Boolean Matrix Decomposition problem that have pragmatic implications. We then present a unified framework for modeling the optimal binary Matrix Decomposition and its variants using binary integer programming. Such modeling allows us to directly adopt the huge body of heuristic solutions and tools developed for binary integer programming. Although the proposed solutions are applicable to any domain of interest, for providing more meaningful discussions and results, in this paper, we present the binary Matrix Decomposition problem in a role engineering context, whose goal is to discover an optimal and correct set of roles from existing permissions, referred to as the role mining problem (RMP). This problem has gained significant interest in recent years as role based access control has become a popular means of enforcing security in databases. We consider several variants of the above basic RMP, including the min-noise RMP, delta-approximate RMP and edge-RMP. Solutions to each of them aid security administrators in specific scenarios. We then model these variants as Boolean Matrix Decomposition and present efficient heuristics to solve them.
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ICDE - Optimal Boolean Matrix Decomposition: Application to Role Engineering
2008 IEEE 24th International Conference on Data Engineering, 2008Co-Authors: Jaideep Vaidya, Vijayalakshmi AtluriAbstract:A Decomposition of a binary Matrix into two matrices gives a set of basis vectors and their appropriate combination to form the original Matrix. Such Decomposition solutions are useful in a number of application domains including text mining, role engineering as well as knowledge discovery. While a binary Matrix can be decomposed in several ways, however, certain Decompositions better characterize the semantics associated with the original Matrix in a succinct but comprehensive way. Indeed, one can find different Decompositions optimizing different criteria matching various semantics. In this paper, we first present a number of variants to the optimal Boolean Matrix Decomposition problem that have pragmatic implications. We then present a unified framework for modeling the optimal binary Matrix Decomposition and its variants using binary integer programming. Such modeling allows us to directly adopt the huge body of heuristic solutions and tools developed for binary integer programming. Although the proposed solutions are applicable to any domain of interest, for providing more meaningful discussions and results, in this paper, we present the binary Matrix Decomposition problem in a role engineering context, whose goal is to discover an optimal and correct set of roles from existing permissions, referred to as the role mining problem (RMP). This problem has gained significant interest in recent years as role based access control has become a popular means of enforcing security in databases. We consider several variants of the above basic RMP, including the min-noise RMP, delta-approximate RMP and edge-RMP. Solutions to each of them aid security administrators in specific scenarios. We then model these variants as Boolean Matrix Decomposition and present efficient heuristics to solve them.
Vijayalakshmi Atluri - One of the best experts on this subject based on the ideXlab platform.
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ICDM - Extended Boolean Matrix Decomposition
2009 Ninth IEEE International Conference on Data Mining, 2009Co-Authors: Jaideep Vaidya, Vijayalakshmi Atluri, Yuan HongAbstract:With the vast increase in collection and storage of data, the problem of data summarization is most critical for effective data management. Since much of this data is categorical in nature, it can be viewed in terms of a Boolean Matrix. Boolean Matrix Decomposition (BMD) has been used to provide concise and interpretable representations of Boolean data sets. A Boolean Matrix can be expressed as a product of two Boolean matrices, where the first Matrix represents a set of meaningful concepts, and the second describes how the observed data can be expressed as combinations of those concepts. Typically, the combination is only in terms of the set union. In other words, a successful Boolean Matrix Decomposition gives a set of concepts and shows how every column of the input data can be expressed as a union of some subset of those concepts. However, this way of modeling only incompletely represents real data semantics. Essentially, it ignores a critical component -- the set difference operation: a column can be expressed as the combination of union of certain concepts as well as the exclusion of other concepts. This has two significant benefits. First, the total number of concepts required to describe the data may itself be reduced. Second, a more succinct summarization may be found for every column. In this paper, we propose the extended Boolean Matrix Decomposition (EBMD) problem, which aims to factor Boolean matrices using both the set union and set difference operations. We study several variants of the problem, show that they are NP-hard, and propose efficient heuristics to solve them. Extensive experimental results demonstrate the power of EBMD.
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optimal boolean Matrix Decomposition application to role engineering
International Conference on Data Engineering, 2008Co-Authors: Jaideep Vaidya, Vijayalakshmi AtluriAbstract:A Decomposition of a binary Matrix into two matrices gives a set of basis vectors and their appropriate combination to form the original Matrix. Such Decomposition solutions are useful in a number of application domains including text mining, role engineering as well as knowledge discovery. While a binary Matrix can be decomposed in several ways, however, certain Decompositions better characterize the semantics associated with the original Matrix in a succinct but comprehensive way. Indeed, one can find different Decompositions optimizing different criteria matching various semantics. In this paper, we first present a number of variants to the optimal Boolean Matrix Decomposition problem that have pragmatic implications. We then present a unified framework for modeling the optimal binary Matrix Decomposition and its variants using binary integer programming. Such modeling allows us to directly adopt the huge body of heuristic solutions and tools developed for binary integer programming. Although the proposed solutions are applicable to any domain of interest, for providing more meaningful discussions and results, in this paper, we present the binary Matrix Decomposition problem in a role engineering context, whose goal is to discover an optimal and correct set of roles from existing permissions, referred to as the role mining problem (RMP). This problem has gained significant interest in recent years as role based access control has become a popular means of enforcing security in databases. We consider several variants of the above basic RMP, including the min-noise RMP, delta-approximate RMP and edge-RMP. Solutions to each of them aid security administrators in specific scenarios. We then model these variants as Boolean Matrix Decomposition and present efficient heuristics to solve them.
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ICDE - Optimal Boolean Matrix Decomposition: Application to Role Engineering
2008 IEEE 24th International Conference on Data Engineering, 2008Co-Authors: Jaideep Vaidya, Vijayalakshmi AtluriAbstract:A Decomposition of a binary Matrix into two matrices gives a set of basis vectors and their appropriate combination to form the original Matrix. Such Decomposition solutions are useful in a number of application domains including text mining, role engineering as well as knowledge discovery. While a binary Matrix can be decomposed in several ways, however, certain Decompositions better characterize the semantics associated with the original Matrix in a succinct but comprehensive way. Indeed, one can find different Decompositions optimizing different criteria matching various semantics. In this paper, we first present a number of variants to the optimal Boolean Matrix Decomposition problem that have pragmatic implications. We then present a unified framework for modeling the optimal binary Matrix Decomposition and its variants using binary integer programming. Such modeling allows us to directly adopt the huge body of heuristic solutions and tools developed for binary integer programming. Although the proposed solutions are applicable to any domain of interest, for providing more meaningful discussions and results, in this paper, we present the binary Matrix Decomposition problem in a role engineering context, whose goal is to discover an optimal and correct set of roles from existing permissions, referred to as the role mining problem (RMP). This problem has gained significant interest in recent years as role based access control has become a popular means of enforcing security in databases. We consider several variants of the above basic RMP, including the min-noise RMP, delta-approximate RMP and edge-RMP. Solutions to each of them aid security administrators in specific scenarios. We then model these variants as Boolean Matrix Decomposition and present efficient heuristics to solve them.
Stephen J Maybank - One of the best experts on this subject based on the ideXlab platform.
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salient object detection via structured Matrix Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017Co-Authors: Houwen Peng, Haibin Ling, Weihua Xiong, Stephen J MaybankAbstract:Low-rank recovery models have shown potential for salient object detection, where a Matrix is decomposed into a low-rank Matrix representing image background and a sparse Matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse Matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured Matrix Decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the Matrix Decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.
Houwen Peng - One of the best experts on this subject based on the ideXlab platform.
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salient object detection via structured Matrix Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017Co-Authors: Houwen Peng, Haibin Ling, Weihua Xiong, Stephen J MaybankAbstract:Low-rank recovery models have shown potential for salient object detection, where a Matrix is decomposed into a low-rank Matrix representing image background and a sparse Matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse Matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured Matrix Decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the Matrix Decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.
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salient object detection via low rank and structured sparse Matrix Decomposition
National Conference on Artificial Intelligence, 2013Co-Authors: Houwen Peng, Weihua Xiong, Congyan LangAbstract:Salient object detection provides an alternative solution to various image semantic understanding tasks such as object recognition, adaptive compression and image retrieval. Recently, low-rank Matrix recovery (LR) theory has been introduced into saliency detection, and achieves impressed results. However, the existing LR-based models neglect the underlying structure of images, and inevitably degrade the associated performance. In this paper, we propose a Low-rank and Structured sparse Matrix Decomposition (LSMD) model for salient object detection. In the model, a tree-structured sparsity-inducing norm regularization is firstly introduced to provide a hierarchical description of the image structure to ensure the completeness of the extracted salient object. The similarity of saliency values within the salient object is then guaranteed by the l∞-norm. Finally, high-level priors are integrated to guide the Matrix Decomposition and enhance the saliency detection. Experimental results on the largest public benchmark database show that our model outperforms existing LR-based approaches and other state-of-the-art methods, which verifies the effectiveness and robustness of the structure cues in our model.
Simon Dixon - One of the best experts on this subject based on the ideXlab platform.
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Towards complex Matrix Decomposition of spectrograms based on the relative phase offsets of harmonic sounds
2014Co-Authors: Holger Kirchhoff, Roland Badeau, Simon DixonAbstract:In this paper we study the relative phase offsets between partials in the sustained part of harmonic sounds and investigate their suitability for complex Matrix Decomposition of spectrograms. We formally introduce this property in a sinusoidal model and visualise the phase relations of a musical instrument. A model of complex Matrix Decomposition in the time-frequency domain is derived and equations for the estimation of the model parameters are provided in the monophonic case. We illustrate the model with the analysis of a monophonic saxophone signal. The results suggest that the phase offset is able to capture inherent time-invariant phase properties of harmonic sounds and outline its potential use for complex Matrix Decomposition.
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ICASSP - TOWARDS COMPLEX Matrix Decomposition OF SPECTROGRAMS BASED ON THE RELATIVE PHASE OFFSETS OF HARMONIC SOUNDS
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Holger Kirchhoff, Roland Badeau, Simon DixonAbstract:In this paper we study the relative phase offsets between partials in the sustained part of harmonic sounds and investigate their suitability for complex Matrix Decomposition of spectrograms. We formally introduce this property in a sinusoidal model and visualise the phase relations of a musical instrument. A model of complex Matrix Decomposition in the time-frequency domain is derived and equations for the estimation of the model parameters are provided in the monophonic case. We illustrate the model with the analysis of a monophonic saxophone signal. The results suggest that the phase offset is able to capture inherent time-invariant phase properties of harmonic sounds and outline its potential use for complex Matrix Decomposition.