Tensor factorization for missing data imputation in medical questionnaires

Dauwels, Justin; Garg, Lalit; Earnest, Arul; Pang, Leong Khai

doi:10.1109/icassp.2012.6288327

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…Questionnaires [28], [29] are instruments or procedures that ask one or more questions and can include items from five main categories: binary responses (e.g., agree/disagree), rating scales (e.g., Likert response scales), multiple or single selection, open-ended comments, and non-question components (e.g., invitation, introduction, closing). Questionnaire analysis can involve analyzing Likert scales [30], [31], text-mining open-ended answers [32]- [38], handling missing data [39]- [42], drawing inferences according to existing answers [43], and cross-analyzing answers to obtain the psychology of audiences [36], [44]. Starting in 2010, questionnaire data analysis began to focus on related factors from non-questionnaire data (e.g., image, voice) to questionnaire data [45], [46].…”

Section: B Technology Development In Dblpmentioning

confidence: 99%

Visualizing Literature Review Theme Evolution on Timeline Maps: Comparison Across Disciplines

et al. 2019

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Data-driven visualization techniques can be utilized to enhance the literature review process across different disciplines. In our work, 910 articles were retrieved using keyword search from bibliographic databases of two different disciplines (computer science: DBLP and medicine: MEDLINE) between 2001 and 2016. These articles' titles were processed using dynamic latent Dirichlet allocation to generate a set of themes/topics, which were subsequently classified and assigned to regions in a spatiotemporal geographical map. Resulting data visualizations from both repositories were manually reviewed by independent annotators. The results from the DBLP and MEDLINE were comparable and, taken together, suggest potential benefits of increased future interaction amongst multidisciplinary fields. Our findings indicate that spiral timeline maps have the potential to help researchers acquire or compare knowledge efficiently without prior domain knowledge.

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Section: B Technology Development In Dblpmentioning

confidence: 99%

Visualizing Literature Review Theme Evolution on Timeline Maps: Comparison Across Disciplines

et al. 2019

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“…The authors have applied this algorithm to an EEG (electroencephalogram) application, where missing data is frequently encountered, for example, owing to disconnections of electrodes. Recently, the same idea was extended and applied to the problem of estimate missing values in medical questionnaires and missing data imputation in road traffic networks . In Ref Tan et al have proposed a Tucker model‐based method for missing traffic data completion where the Tucker factors and the core tensor are optimized to fit the available information.…”

Section: Selected Applicationsmentioning

confidence: 99%

“…There are also other fields of research that could be benefit from the methods discussed in this paper, for example, by learning the appropriate Kronecker dictionaries, our tensor completion algorithm could be applied to missing data problems for EEG recordings, to estimate missing values in medical questionnaires, or to missing data imputation in road traffic networks…”

Section: Selected Applicationsmentioning

confidence: 99%

Multidimensional compressed sensing and their applications

Caiafa

Cichocki

2013

WIREs Data Min & Knowl

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Compressed sensing (CS) comprises a set of relatively new techniques that exploit the underlying structure of data sets allowing their reconstruction from compressed versions or incomplete information. CS reconstruction algorithms are essentially nonlinear, demanding heavy computation overhead and large storage memory, especially in the case of multidimensional signals. Excellent review papers discussing CS state-of-the-art theory and algorithms already exist in the literature, which mostly consider data sets in vector forms. In this paper, we give an overview of existing techniques with special focus on the treatment of multidimensional signals (tensors). We discuss recent trends that exploit the natural multidimensional structure of signals (tensors) achieving simple and efficient CS algorithms. The Kronecker structure of dictionaries is emphasized and its equivalence to the Tucker tensor decomposition is exploited allowing us to use tensor tools and models for CS. Several examples based on real world multidimensional signals are presented, illustrating common problems in signal processing such as the recovery of signals from compressed measurements for magnetic resonance imaging (MRI) signals or for hyper-spectral imaging, and the tensor completion problem (multidimensional inpainting). Conflict of interest: The authors have declared no conflicts of interest for this article. are based on sampling analog signals at a high Nyquist rate followed by a compression coding stage keeping in memory only the essential information about signals, i.e. storing only the most significant coefficients. Compressed sensing (CS) theory suggests the compelling idea that the sampling process can be greatly simplified by taking only few informative measurements from which full data sets can be reconstructed almost perfectly. 2,3 CS theory has revolutionized signal processing, for instance, new imaging sensor paradigms were developed on the basis of CS, 4-8 and some classical image processing problems were approached using results of CS theory as in the case of denoising, 9-11 inpainting, 12-16 superresolution, 15,17-19 deblurring, 20-23 and others.Originally, CS theory was developed for digital signals, in particular for signals that are mapped to a one-dimensional (1D) array (vector). Powerful reconstruction techniques were developed for relatively small sized vectors which are measured by using random matrices. There are excellent review papers in the literature that cover the state-of-the-art Volume 3,

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“…Methods such as Singular Value Decomposition (SVD) and Canonical Polyadic (CP) decomposition are usually applied to find low-dimensional representation of multivariate systems. To perform the decomposition in presence of missing data, we use Fixed Point Continuation with Approximate SVD (FPCA) [15] and CP Weighted OPTimization (CP-WOPT) [16,17]. For benchmarking, we compare their performance with Bayesian Principal Component Analysis (BPCA) [2] and historical averages.…”

Section: Fig 1: Road Network In Singapore (Outram To Changi)mentioning

confidence: 99%

Low-dimensional models for missing data imputation in road networks

Asif

Mitrović

Garg

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Intelligent transport systems (ITS) require data with high spatial and temporal resolution for applications such as modeling, traffic management, prediction and route guidance. However, field data is usually quite sparse. This problem of missing data severely limits the effectiveness of ITS. Missing values are usually imputed by either using historical data of the road or current information from neighboring links. In most scenarios, information from some or all of neighboring links might not be available.Furthermore, historical data may also be incomplete. To overcome these issues, we propose methods which can construct low-dimensional representation of large and diverse networks, in presence of missing historical and neighboring data. We use these low-dimensional models to reconstruct data profiles for road segments, and impute missing values. To this end we use Fixed Point Continuation with Approximate SVD (FPCA) and Canonical Polyadic (CP) decomposition for incomplete tensors to solve the problem of missing data. We apply these methods to expressways and a large urban road network to assess their performance for different scenarios.Index Terms-Missing data in large networks, low-dimensional models

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Tensor factorization for missing data imputation in medical questionnaires

Cited by 14 publications

References 11 publications

Visualizing Literature Review Theme Evolution on Timeline Maps: Comparison Across Disciplines

Visualizing Literature Review Theme Evolution on Timeline Maps: Comparison Across Disciplines

Multidimensional compressed sensing and their applications

Low-dimensional models for missing data imputation in road networks

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