An Efficient Greedy Method for Unsupervised Feature Selection

Farahat, Ahmed K.; Ghodsi, Ali; Kamel, Mohamed S.

doi:10.1109/icdm.2011.22

Cited by 76 publications

(48 citation statements)

References 8 publications

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“…We included scanner strength, voxel size, patient age and gender as covariates in the analysis. The SVM-RFE method allows one to minimize redundant and extraneous features that could potentially degrade classifier performance (Farahat et al, 2011). SVM-RFE works backwards from the initial set of features and eliminates the least "useful" feature on each recursive pass.…”

Section: Machine Learning Methods and Analysismentioning

confidence: 99%

Machine learning classification of mesial temporal sclerosis in epilepsy patients

Rudie¹,

Colby²,

Salamon

2015

Epilepsy Research

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a b s t r a c tBackground and purpose: Novel approaches applying machine-learning methods to neuroimaging data seek to develop individualized measures that will aid in the diagnosis and treatment of brain-based disorders such as temporal lobe epilepsy (TLE). Using a large cohort of epilepsy patients with and without mesial temporal sclerosis (MTS), we sought to automatically classify MTS using measures of cortical morphology, and to further relate classification probabilities to measures of disease burden. Materials and methods: Our sample consisted of high-resolution T1 structural scans of 169 adults with epilepsy collected across five different 1.5 T and four different 3 T scanners at UCLA. We applied a multiple support vector machine recursive feature elimination algorithm to morphological measures generated from FreeSurfer's automated segmentation and parcellation in order to classify Epilepsy patients with MTS (n = 85) from those without MTS (N = 84). Results: In addition to hippocampal volume, we found that alterations in cortical thickness, surface area, volume and curvature in inferior frontal and anterior and inferior temporal regions contributed to a classification accuracy of up to 81% (p = 1.3 × 10 −17 ) in identifying MTS. We also found that MTS classification probabilities were associated with a longer duration of disease for epilepsy patients both with and without MTS. Conclusions: In addition to implicating extra-hippocampal involvement of MTS, these findings shed further light on the pathogenesis of TLE and may ultimately assist in the development of automated tools that incorporate multiple neuroimaging measures to assist clinicians in detecting more subtle cases of TLE and MTS.

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Section: Machine Learning Methods and Analysismentioning

confidence: 99%

Machine learning classification of mesial temporal sclerosis in epilepsy patients

Rudie¹,

Colby²,

Salamon

2015

Epilepsy Research

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“…In connection to the unsupervised feature selection problem, a variant of the greedy algorithm presented in this paper has previously been proposed for the unsupervised feature selection problem [27,28] where it has shown superior performance to other state-of-the-art methods for unsupervised feature selection. However, the algorithm proposed by Farahat et al [27,28] is centralized and it cannot be easily extended to handle big data that are massively distributed across different machines.…”

Section: Comparison With Related Workmentioning

confidence: 98%

“…However, the algorithm proposed by Farahat et al [27,28] is centralized and it cannot be easily extended to handle big data that are massively distributed across different machines.…”

Section: Comparison With Related Workmentioning

confidence: 99%

“…The minimization of this criterion is a combinatorial optimization problem whose optimal solution can be obtained in O n l mnl [6]. This section describes a deterministic greedy algorithm for optimizing this criterion, which extends the greedy method for unsupervised feature selection recently proposed by Farahat et al [27,28]. First, a recursive formula for the CSS criterion is presented, and then the greedy CSS algorithm is described in details.…”

Section: Greedy Column Subset Selectionmentioning

confidence: 99%

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Greedy column subset selection for large-scale data sets

et al. 2014

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In today's information systems, the availability of massive amounts of data necessitates the development of fast and accurate algorithms to summarize these data and represent them in a succinct format. One crucial problem in big data analytics is the selection of representative instances from large and massively distributed data, which is formally known as the Column Subset Selection problem. The solution to this problem enables data analysts to understand the insights of the data and explore its hidden structure. The selected instances can also be used for data preprocessing tasks such as learning a low-dimensional embedding of the data points or computing a low-rank approximation of the corresponding matrix. This paper presents a fast and accurate greedy algorithm for large-scale column subset selection. The algorithm minimizes an objective function, which measures the reconstruction error of the data matrix based on the subset of selected columns. The paper first presents a centralized greedy algorithm for column subset selection, which depends on a novel recursive formula for calculating the reconstruction error of the data matrix. The paper then presents a MapReduce algorithm, which selects a few representative columns from a matrix whose columns are massively distributed across several commodity machines. The algorithm first A preliminary version of this paper appeared as [26]. This work was completed while the second author was at the University of Waterloo.123 A. K. Farahat et al. learns a concise representation of all columns using random projection, and it then solves a generalized column subset selection problem at each machine in which a subset of columns are selected from the sub-matrix on that machine such that the reconstruction error of the concise representation is minimized. The paper demonstrates the effectiveness and efficiency of the proposed algorithm through an empirical evaluation on benchmark data sets.

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“…Besides, a number of methods that are analytically or computationally manageable in low dimensional space may become completely intractable when the number of features reaches thousands or even more [3]. Therefore, reducing the data dimension is an indispensable part for data mining and machine learning tasks [4].…”

Section: Introductionmentioning

confidence: 99%