Email Surveillance Using Non-negative Matrix Factorization

Berry, Michael W.; Browne, Murray

doi:10.1007/s10588-005-5380-5

Cited by 104 publications

(58 citation statements)

References 13 publications

(29 reference statements)

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“…Next, to gain intuition for the (non-)uniformity properties of statistical leverage scores in a typical application, consider a term-document matrix derived from the publicly-released Enron electronic mail collection [169], which is typical of the type of data set to which SVD-based latent semantic analysis (LSA) methods [170] have been applied. This is a 65, 031 × 92, 133 matrix, as described in [169], and let us choose the rank parameter as k = 10.…”

Section: More Recent Perspectives On Statistical Leveragementioning

confidence: 99%

Randomized Algorithms for Matrices and Data

Mahoney¹

2012

Chapman &Amp; Hall/CRC Data Mining and Knowledge Discovery Series

335

373

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Randomized algorithms for very large matrix problems have received a great deal of attention in recent years. Much of this work was motivated by problems in large-scale data analysis, largely since matrices are popular structures with which to model data drawn from a wide range of application domains, and this work was performed by individuals from many different research communities. While the most obvious benefit of randomization is that it can lead to faster algorithms, either in worst-case asymptotic theory and/or numerical implementation, there are numerous other benefits that are at least as important. For example, the use of randomization can lead to simpler algorithms that are easier to analyze or reason about when applied in counterintuitive settings; it can lead to algorithms with more interpretable output, which is of interest in applications where analyst time rather than just computational time is of interest; it can lead implicitly to regularization and more robust output; and randomized algorithms can often be organized to exploit modern computational architectures better than classical numerical methods.This monograph will provide a detailed overview of recent work on the theory of randomized matrix algorithms as well as the application of those ideas to the solution of practical problems in large-scale data analysis. Throughout this review, an emphasis will be placed on a few simple core ideas that underlie not only recent theoretical advances but also the usefulness of these tools in large-scale data applications. Crucial in this context is the connection with the concept of statistical leverage. This concept has long been used in statistical regression diagnostics to identify outliers; and it has recently proved crucial in the development of improved worst-case matrix algorithms that are also amenable to high-quality numerical implementation and that are useful to domain scientists. This connection arises naturally when one explicitly decouples the effect of randomization in these matrix algorithms from the underlying linear algebraic structure. This decoupling also permits much finer control in the application of randomization, as well as the easier exploitation of domain knowledge.Most of the review will focus on random sampling algorithms and random projection algorithms for versions of the linear least-squares problem and the low-rank matrix approximation problem. These two problems are fundamental in theory and ubiquitous in practice. Randomized methods solve these problems by constructing and operating on a randomized sketch of the input matrix Afor random sampling methods, the sketch consists of a small number of carefully-sampled and rescaled columns/rows of A, while for random projection methods, the sketch consists of a small number of linear combinations of the columns/rows of A. Depending on the specifics of the situation, when compared with the best previously-existing deterministic algorithms, the resulting randomized algorithms have worst-case running time that is asympt...

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Section: More Recent Perspectives On Statistical Leveragementioning

confidence: 99%

Randomized Algorithms for Matrices and Data

Mahoney¹

2012

Chapman &Amp; Hall/CRC Data Mining and Knowledge Discovery Series

335

373

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“…Berry and Browne [3] apply nonnegative matrix factorizations to discover concepts and topics in the Enron corpus. They discuss results of topic detection and message clustering in the context of published Enron business practices and activities.…”

Section: Enron Data and Social Network Analysismentioning

confidence: 99%

Temporal analysis of social networks using three-way DEDICOM.

Bader¹,

Harshman²,

Kolda

2006

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DEDICOM is an algebraic model for analyzing intrinsically asymmetric relationships, such as the balance of trade among nations or the flow of information among organizations or individuals. It provides information on latent components in the data that can be regarded as "properties" or "aspects" of the objects, and it finds a few patterns that can be combined to describe many relationships among these components. When we apply this technique to adjacency matrices arising from directed graphs, we obtain a smaller graph that gives an idealized description of its patterns. Three-way DEDICOM is a higher-order extension of the model that has certain uniqueness properties. It allows for a third mode of the data, such as time, and permits the analysis of semantic graphs. We present an improved algorithm for computing three-way DEDICOM on sparse data and demonstrate it by applying it to the adjacency tensor of a semantic graph with time-labeled edges. Our application uses the Enron email corpus, from which we construct a semantic graph corresponding to email exchanges among Enron personnel over a series of 44 months. Meaningful patterns are recovered in which the representation of asymmetries adds insight into the social networks at Enron.

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“…Extracting topics from a temporally changing text collection has received some attention lately, for instance by [7] and also touched by [8]. These works investigate text streams that contain documents that can be assigned a timestamp y.…”

Section: Temporal Topic Detectionmentioning

confidence: 99%

“…The approaches to temporal topic detection presented in [7] and [8] employ latent factor methods to extract distinct topics for each time interval, and then compare the found topics at succeeding time intervals to link the topics over time to form temporal topics. We extract topics from each sub-collection C k using a PLSA-model [5].…”

Section: Ck With Parameters (H = {P(t Iz)k P(dlz)k P(z)k} Needmentioning

confidence: 99%

Temporal analysis of text data using latent variable models

Mølgaard¹,

Larsen²,

Goutte

2009

2009 IEEE International Workshop on Machine Learning for Signal Processing

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Detecting and tracking of temporal data is an important task in multiple applications. In this paper we study temporal text mining methods for Music Information Retrieval. We compare two ways of detecting the temporal latent semantics of a corpus extracted from Wikipedia, using a stepwise Probabilistic Latent Semantic Analysis (PLSA) approach and a global multiway PLSA method. The analysis indicates that the global analysis method is able to identify relevant trends which are difficult to get using a step-by-step approach. Furthermore we show that inspection of PLSA models with different number of factors may reveal the stability of temporal clusters making it possible to choose the relevant number of factors.

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Email Surveillance Using Non-negative Matrix Factorization

Cited by 104 publications

References 13 publications

Randomized Algorithms for Matrices and Data

Randomized Algorithms for Matrices and Data

Temporal analysis of social networks using three-way DEDICOM.

Temporal analysis of text data using latent variable models

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