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Binary Matrix Factorization with Applications
"... An interesting problem in Nonnegative Matrix Factorization (NMF) is to factorize the matrix X which is of some specific class, for example, binary matrix. In this paper, we extend the standard NMF to Binary Matrix Factorization (BMF for short): given a binary matrix X, we want to factorize X into tw ..."
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Cited by 13 (1 self)
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An interesting problem in Nonnegative Matrix Factorization (NMF) is to factorize the matrix X which is of some specific class, for example, binary matrix. In this paper, we extend the standard NMF to Binary Matrix Factorization (BMF for short): given a binary matrix X, we want to factorize X
ESTIMATION OF THE PERMANENT OF A BINARY MATRIX
"... Let A be a square matrix over an arbitrary field. The permanent of the matrix A is defined as the algebraic sum of the products of any N elements of the matrix, one in each row and column. Symbol: perm A – the permanent of the matrix A. Obviosly, the permanent is similar to the determinant, but with ..."
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Let A be a square matrix over an arbitrary field. The permanent of the matrix A is defined as the algebraic sum of the products of any N elements of the matrix, one in each row and column. Symbol: perm A – the permanent of the matrix A. Obviosly, the permanent is similar to the determinant
Stochastic Approach to Binary Matrix Partitioning for Phylogenetic Networks
"... In this research we introduce the problem of the binary matrix partitioning in a biological context. Our idea is to use SNP matrix to construct a set of phylogenetic networks to retrieve underlying biological meanings and dependencies. We emphasize stochastic methods for matrix clustering and briefl ..."
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In this research we introduce the problem of the binary matrix partitioning in a biological context. Our idea is to use SNP matrix to construct a set of phylogenetic networks to retrieve underlying biological meanings and dependencies. We emphasize stochastic methods for matrix clustering
Mining Discrete Patterns via Binary Matrix Factorization
"... Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rankone binary matrix approximations that identify the dominant patterns of the data, while preserving its discrete property. A best approximation on such data has a minimum set of inconsis ..."
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Cited by 6 (0 self)
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Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rankone binary matrix approximations that identify the dominant patterns of the data, while preserving its discrete property. A best approximation on such data has a minimum set
Weighted RankOne Binary Matrix Factorization
"... Mining discrete patterns in binary data is important for many data analysis tasks, such as data sampling, compression, and clustering. An example is that replacing individual records with their patterns would greatly reduce data size and simplify subsequent data analysis tasks. As a straightforward ..."
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Cited by 3 (0 self)
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approach, rankone binary matrix approximation has been actively studied recently for mining discrete patterns from binary data. It factorizes a binary matrix into the multiplication of one binary pattern vector and one binary presence vector, while minimizing mismatching entries. However, this approach
On Undetected Error Probability of Binary Matrix Ensembles
, 2007
"... In this paper, an analysis of the undetected error probability of ensembles of m×n binary matrices is presented. Two ensembles are considered: One is an ensemble of dense matrices, while the other is an ensemble of sparse matrices. The main contributions of this work are (i) derivation of the erro ..."
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Cited by 4 (2 self)
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In this paper, an analysis of the undetected error probability of ensembles of m×n binary matrices is presented. Two ensembles are considered: One is an ensemble of dense matrices, while the other is an ensemble of sparse matrices. The main contributions of this work are (i) derivation
On Undetected Error Probability of Binary Matrix Ensembles
"... Abstract — In this paper, analysis on undetected error probability of ensembles of m × n binary matricies is presented. Two ensembles are considered: One is an ensemble of dense matrices and another is an ensemble of sparse matrices. The main contributions of this work are (i) derivation of the erro ..."
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Abstract — In this paper, analysis on undetected error probability of ensembles of m × n binary matricies is presented. Two ensembles are considered: One is an ensemble of dense matrices and another is an ensemble of sparse matrices. The main contributions of this work are (i) derivation
On the Size and Recovery of Submatrices of Ones in a Random Binary Matrix
"... Binary matrices, and their associated submatrices of 1s, play a central role in the study of random bipartite graphs and in core data mining problems such as frequent itemset mining (FIM). Motivated by these connections, this paper addresses several statistical questions regarding submatrices of 1s ..."
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Cited by 9 (2 self)
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in a random binary matrix with independent Bernoulli entries. We establish a threepoint concentration result, and a related probability bound, for the size of the largest square submatrix of 1s in a square Bernoulli matrix, and extend these results to nonsquare matrices and submatrices with fixed
A New Digital Encryption Scheme: Binary Matrix Rotations Encryption Algorithm
"... Abstract: In today’s world most of the communication is done using electronic media. Data security plays an important role in such communication. So, there’s a requirement for a stronger encoding that is extremely exhausting to crack. Completely different encrypted algorithms are planned thus far to ..."
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to come up with encrypted information of original information. In this we have proposed a new replacement algorithmic rule for Digital encoding called as “Binary Matrix Rotations Technique ” (BMR) which reduces size of the data as well as form of the data. The experimental results show that the new theme
A Binary Matrix Factorization Algorithm for Protein Complex Prediction
"... We propose a binary matrix factorization (BMF) algorithm under the Bayesian YingYang (BYY) harmony learning, to detect protein complexes by clustering the proteins which share similar interactions through factorizing the binary adjacent matrix of the proteinprotein interaction (PPI) network. The p ..."
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Cited by 1 (1 self)
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We propose a binary matrix factorization (BMF) algorithm under the Bayesian YingYang (BYY) harmony learning, to detect protein complexes by clustering the proteins which share similar interactions through factorizing the binary adjacent matrix of the proteinprotein interaction (PPI) network
Results 1  10
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271,774