Comparison of Neural Network Boolean Factor Analysis Method with Some Other Dimension Reduction Methods on Bars Problem

Húsek, Dušan; Moravec, Pavel; Snåšel, Václav; Frolov, Alexander A.; Řezanková, Hana; Polyakov, Pavel

doi:10.1007/978-3-540-77046-6_29

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…There are several well known methods and algorithms for factorization of real data but many application areas including information retrieval, pattern recognition and data mining require processing of binary rather than real data (see [13,25,42]). …”

Section: Matrix Factorization For Feature Selection and Classificationmentioning

confidence: 99%

Matrix Factorization Approach for Feature Deduction and Design of Intrusion Detection Systems

Snášel

Platoš

Krömer

et al. 2008

2008 the Fourth International Conference on Information Assurance and Security

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Current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this research is to identify important input features in building an IDS that is computationally efficient and effective. This paper propose a novel matrix factorization approach for feature deduction and design of intrusion detection systems. Experiment results indicate that the proposed method is efficient.

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Section: Matrix Factorization For Feature Selection and Classificationmentioning

confidence: 99%

Matrix Factorization Approach for Feature Deduction and Design of Intrusion Detection Systems

Snášel

Platoš

Krömer

et al. 2008

2008 the Fourth International Conference on Information Assurance and Security

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“…All the elements in X, A, and B are either 0 or 1. n is defined to be the number of underlying factors and is assumed to be considerably smaller than the number of objects T . BFA methods aim to find a feasible decomposition minimizing n. Frolov et al study the problem of factoring a binary matrix in a series of papers [20], [21], [22] using Hopfield neural networks. This approaches are based on heuristics and do not provide much theoretical insight regarding the properties of the resulting decomposition.…”

Section: Related Workmentioning

confidence: 99%

Binary Inference for Primary User Separation in Cognitive Radio Networks

Nguyen¹,

Zheng²,

Zheng³

et al. 2013

IEEE Trans. Wireless Commun.

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Abstract-Spectrum sensing receives much attention recently in the cognitive radio (CR) network research, i.e., secondary users (SUs) constantly monitor channel condition to detect the presence of the primary users (PUs). In this paper, we go beyond spectrum sensing and introduce the PU separation problem, which concerns with the issues of distinguishing and characterizing PUs in the context of collaborative spectrum sensing and monitor selection. The observations of monitors are modeled as boolean OR mixtures of underlying binary sources for PUs. We first justify the use of the binary OR mixture model as opposed to the traditional linear mixture model through simulation studies. Then we devise a novel binary inference algorithm for PU separation. Not only PU-SU relationship are revealed, but PUs' transmission statistics and activities at each time slot can also be inferred. Simulation results show that without any prior knowledge regarding PUs' activities, the algorithm achieves high inference accuracy even in the presence of noisy measurements.

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“…Zero Error Case: If X is perfectly observed, containing no noise, we have p e = 0 and α i = x i − X i = 0, or equivalently, x i = X i . The integer programming problem in (13) can now be simplified as:…”

Section: The Inverse Problemmentioning

confidence: 99%

“…All the elements in X, A, and B are either 0 or 1. n is defined to be the number of underlying factors and is assumed to be considerably smaller than the number of objects T . BFA methods aim to find a feasible decomposition minimizing n. neural networks [12], [10], [13]. This approach is based on heuristic and do not provide much theoretical insight regarding the properties of the resulting decomposition.…”

Section: Related Workmentioning

confidence: 99%

Binary Independent Component Analysis With or Mixtures

Nguyen

Zheng

2011

IEEE Trans. Signal Process.

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Abstract-Independent component analysis (ICA) is a computational method for separating a multivariate signal into subcomponents assuming the mutual statistical independence of the non-Gaussian source signals. The classical Independent Components Analysis (ICA) framework usually assumes linear combinations of independent sources over the field of realvalued numbers R. In this paper, we investigate binary ICA for OR mixtures (bICA), which can find applications in many domains including medical diagnosis, multi-cluster assignment, Internet tomography and network resource management. We prove that bICA is uniquely identifiable under the disjunctive generation model, and propose a deterministic iterative algorithm to determine the distribution of the latent random variables and the mixing matrix. The inverse problem concerning inferring the values of latent variables are also considered along with noisy measurements. We conduct an extensive simulation study to verify the effectiveness of the propose algorithm and present examples of real-world applications where bICA can be applied.

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Comparison of Neural Network Boolean Factor Analysis Method with Some Other Dimension Reduction Methods on Bars Problem

Cited by 8 publications

References 9 publications

Matrix Factorization Approach for Feature Deduction and Design of Intrusion Detection Systems

Matrix Factorization Approach for Feature Deduction and Design of Intrusion Detection Systems

Binary Inference for Primary User Separation in Cognitive Radio Networks

Binary Independent Component Analysis With or Mixtures

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