The Unreasonable Effectiveness of Data

Halevy, Alon; Norvig, Peter; Pereira, Fernando

doi:10.1109/mis.2009.36

Cited by 1,356 publications

(743 citation statements)

References 10 publications

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“…When dealing with today's large-scale data sets, many data mining practitioners therefore often abandon deterministic approaches and resort to randomized approaches. However, huge data such as collections of online books at Amazon TM , image repositories at Flickr TM or Google TM , or personal health records [16,19,23,24,29] are becoming ever more common and thus pose a challenge to research on interpretable matrix factorization.…”

Section: Introductionmentioning

confidence: 99%

Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study

Thurau¹,

Kersting

Bauckhage³

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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Low-rank approximations which are computed from selected rows and columns of a given data matrix have attracted considerable attention lately. They have been proposed as an alternative to the SVD because they naturally lead to interpretable decompositions which was shown to be successful in application such as fraud detection, fMRI segmentation, and collaborative filtering. The CUR decomposition of large matrices, for example, samples rows and columns according to a probability distribution that depends on the Euclidean norm of rows or columns or on other measures of statistical leverage. At the same time, there are various deterministic approaches that do not resort to sampling and were found to often yield factorization of superior quality with respect to reconstruction accuracy. However, these are hardly applicable to large matrices as they typically suffer from high computational costs. Consequently, many practitioners in the field of data mining have abandon deterministic approaches in favor of randomized ones when dealing with today's large-scale data sets. In this paper, we empirically disprove this prejudice. We do so by introducing a novel, linear-time, deterministic CUR approach that adopts the recently introduced Simplex Volume Maximization approach for column selection. The latter has already been proven to be successful for NMF-like decompositions of matrices of billions of entries. Our exhaustive empirical study on more than 30 synthetic and real-world data sets demonstrates that it is also beneficial for CUR-like decompositions. Compared to other deterministic CUR-like methods, it provides comparable reconstruction quality but operates much faster so that it easily scales to matrices of billions of elements. Compared to sampling-based methods, it provides competitive reconstruction quality while staying in the same run-time complexity class.

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Section: Introductionmentioning

confidence: 99%

Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study

Thurau¹,

Kersting

Bauckhage³

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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“…In the last several years, an active and inventive group at Google, possibly inspired by Halevy, Norvig, and Pereira [37], collected and analyzed millions of tables harvested from the web [1,38,39]. Visual verification of their results has necessarily been restricted to much smaller samples.…”

Section: Physical Structure Extractionmentioning

confidence: 99%

Converting heterogeneous statistical tables on the web to searchable databases

Embley

Krishnamoorthy

Nagy

et al. 2016

IJDAR

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Much of the world's quantitative data resides in scattered web tables. For a meaningful role in Big Data analytics, the facts reported in these tables must be brought into a uniform framework. Based on a formalization of header-indexed tables, we proffer an algorithmic solution to end-to-end table processing for a large class of humanreadable tables. The proposed algorithms transform headerindexed tables to a category table format that maps easily to a variety of industry-standard data stores for query processing. The algorithms segment table regions based on the unique indexing of the data region by header paths, classify table cells, and factor header category structures of two-dimensional as well as the less common multidimensional tables. Experimental evaluations substantiate the algorithmic approach to processing heterogeneous tables. As demonstrable results, the algorithms generate queryable relational database tables and semantic-web triple stores. Application of our algorithms to 400 web tables randomly selected from diverse sources shows that the algorithmic solution automates end-to-end table processing. document analysis table segmentation table analysis table header factoring end-to-end table processing· table headers queries over table data ___________________________________________ Keywords

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“…Do other types of anomaly detectors, or more generally, learning algorithms for other data mining tasks also exhibit the gravity-defiant behaviour? In a complex domain such as natural language processing, millions of additional data has been shown to continue to improve the performance of trained models (Halevy et al 2009;Banko and Brill 2001). Is this the domain for which algorithms always comply with the learning curve?…”

Section: Implications and Potential Future Workmentioning

confidence: 99%

Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors

et al. 2016

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Conventional wisdom in machine learning says that all algorithms are expected to follow the trajectory of a learning curve which is often colloquially referred to as 'more data the better'. We call this 'the gravity of learning curve', and it is assumed that no learning algorithms are 'gravity-defiant'. Contrary to the conventional wisdom, this paper provides the theoretical analysis and the empirical evidence that nearest neighbour anomaly detectors are gravity-defiant algorithms.

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The Unreasonable Effectiveness of Data

Cited by 1,356 publications

References 10 publications

Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study

Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study

Converting heterogeneous statistical tables on the web to searchable databases

Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors

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