Using Dimensionality Reduction Method for Binary Data to Questionnaire Analysis

Mažgut, Jakub; Paulinyov, Martina; Tiňo, Peter

doi:10.1007/978-3-642-25929-6_14

Cited by 3 publications

(3 citation statements)

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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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“…, which render affordable downstream inference tasks such as regression, prediction, interpolation, classification, or, clustering; see e.g., [10], [12], [16], [17], [23], [26]. Aiming at a related objective, the present work builds on three unsupervised categorical models that are described next.…”

Section: Preliminaries and Problem Statementmentioning

confidence: 99%

“…It is however primarily designed to sketch high-dimensional data with analogamplitude values, and does not suit categorical data emerging for instance, with recommender systems. Categorical PCA seeks a low-dimensional sketch of the high-dimensional categorical data to render affordable downstream machine learning tasks such as imputation, classification, and clustering; see e.g., [10], [12], [16], [17], [23], [26], [31]. However, the growing scale of nowadays 'Big Data' applications, such as recommender systems (e.g., NetFlix) with millions of users rating thousands of movies, pose extra challenges: (c1) the sheer volume of data approaches the computational and storage limits; (c2) new releases demand real-time processing for recommendations; and (c3) absent data entries, corresponding to missing user ratings.…”

Section: Introductionmentioning

confidence: 99%

Online Categorical Subspace Learning for Sketching Big Data with Misses

Shen

Mardani

Giannakis

2017

IEEE Trans. Signal Process.

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Abstract-With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to address in this context include the sheer volume of data that may consist of categorical samples, the typically streaming format of acquisition, and the possibly missing entries. To cope with these challenges, the present paper develops a novel categorical subspace learning approach to unravel the latent structure for three prominent categorical (bilinear) models, namely, Probit, Tobit, and Logit. The deterministic Probit and Tobit models treat data as quantized values of an analog-valued process lying in a low-dimensional subspace, while the probabilistic Logit model relies on low dimensionality of the data log-likelihood ratios. Leveraging the low intrinsic dimensionality of the sought models, a rank regularized maximum-likelihood estimator is devised, which is then solved recursively via alternating majorizationminimization to sketch high-dimensional categorical data 'on the fly.' The resultant procedure alternates between sketching the new incomplete datum and refining the latent subspace, leading to lightweight first-order algorithms with highly parallelizable tasks per iteration. As an extra degree of freedom, the quantization thresholds are also learned jointly along with the subspace to enhance the predictive power of the sought models. Performance of the subspace iterates is analyzed for both infinite and finite data streams, where for the former asymptotic convergence to the stationary point set of the batch estimator is established, while for the latter sublinear regret bounds are derived for the empirical cost. Simulated tests with both synthetic and real-world datasets corroborate the merits of the novel schemes for real-time movie recommendation and chess-game classification.

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