Dynamic Boolean Matrix Factorizations
Pauli Miettinen
- pp 519-528
Reads0
Chats0
TLDR
This paper proposes a method to dynamically update the Boolean matrix factorization when new data is added to the data base and is extended with a mechanism to improve the factorization with a trade-off in speed of computation.Abstract:
Boolean matrix factorization is a method to decompose a binary matrix into two binary factor matrices. Akin to other matrix factorizations, the factor matrices can be used for various data analysis tasks. Many (if not most) real-world data sets are dynamic, though, meaning that new information is recorded over time. Incorporating this new information into the factorization can require a re-computation of the factorization -- something we cannot do if we want to keep our factorization up-to-date after each update. This paper proposes a method to dynamically update the Boolean matrix factorization when new data is added to the data base. This method is extended with a mechanism to improve the factorization with a trade-off in speed of computation. The method is tested with a number of real-world and synthetic data sets including studying its efficiency against off-line methods. The results show that with good initialization the proposed online and dynamic methods can beat the state-of-the-art offline Boolean matrix factorization algorithms.read more
Citations
More filters
Journal ArticleDOI
A Unifying Framework for Mining Approximate Top-k Binary Patterns
TL;DR: This work reviews several greedy algorithms, and discusses PANDA+, an algorithmic framework able to optimize different cost functions generalized into a unifying formulation, and evaluates the goodness of the algorithm by measuring the quality of the extracted patterns.
Journal ArticleDOI
Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis
TL;DR: This article focuses on automatic detection of behavioral patterns from the trajectory data of an individual for activity identification as well as daily routine discovery and proposes a novel nominal matrix factorization method under a Bayesian framework with Lasso to extract highly interpretable daily routines.
Proceedings ArticleDOI
Identifying Dynamics of Brain Function Via Boolean Matrix Factorization
TL;DR: Two hierarchical frameworks capable of identifying cortical activity that i and ii exhibit “common” spatio-temporal characteristics across trials of a given task (or classes of similar tasks) are presented.
Design of Virtual Local Area Network Scheme Based on Genetic Optimization and Visual Analysis.
Igor Saenko,Igor Kotenko +1 more
TL;DR: A number of improvements were implemented in the proposed genetic algorithm, concerning the formation of initial population, kind of fitness function, coding chromosomes, and operation of crossing and mutation.
Book ChapterDOI
A Genetic Approach for Virtual Computer Network Design
Igor Saenko,Igor Kotenko +1 more
TL;DR: The paper formulates the virtual subnet design task and proposes genetic algorithms as a means to solve it and shows that the problem considered is related to one of the forms of Boolean Matrix Factorization.
References
More filters
Journal ArticleDOI
The Discrete Basis Problem
TL;DR: This paper describes a matrix decomposition formulation for Boolean data, the Discrete Basis Problem, and gives a simple greedy algorithm for solving it and shows how it can be solved using existing methods.
Book ChapterDOI
The discrete basis problem
TL;DR: This paper describes a matrix decomposition formulation for Boolean data, the Discrete Basis Problem, and gives a simple greedy algorithm for solving it and shows how it can be solved using existing methods.
Proceedings ArticleDOI
Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization
Ankan Saha,Vikas Sindhwani +1 more
TL;DR: An online nonnegative matrix factorizations framework is proposed to capture the evolution and emergence of themes in unstructured text under a novel temporal regularization framework and is able to rapidly capture emerging themes, track existing topics over time while maintaining temporal consistency and continuity in user views.
Proceedings ArticleDOI
Multi-assignment clustering for Boolean data
TL;DR: In this article, a generative method for clustering vectorial data, where each object can be assigned to multiple clusters, is proposed, which decomposes the observed data into the contributions of individual clusters and infers their parameters.
Journal ArticleDOI
Binary matrix factorization for analyzing gene expression data
TL;DR: This paper presents a new biclustering model using Binary Matrix Factorization (BMF), a new variant rooted from non-negative matrix factorization (NMF), and proves a new boundedness property of NMF.