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Analysis of institutional authors

Puerto-Santana, CCorresponding AuthorLarranaga, PAuthorBielza, CAuthor

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August 22, 2022
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Autoregressive Asymmetric Linear Gaussian Hidden Markov Models

Publicated to: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. 44 (9): 4642-4658 - 2022-09-01 44(9), DOI: 10.1109/TPAMI.2021.3068799

Authors:

Puerto-Santana, C; Larrañaga, P; Bielza, C
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Affiliations

Aingura IIoT, San Sebastian 20009, Spain - Author
Univ Politecn Madrid, Madrid 28040, Spain - Author

Abstract

In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each state of the process. Asymmetric hidden Markov models fulfil this dynamical requirement and provide a framework where the trend of the process can be expressed as a latent variable. In this paper, we modify these recent asymmetric hidden Markov models to have an asymmetric autoregressive component in the case of continuous variables, allowing the model to choose the order of autoregression that maximizes its penalized likelihood for a given training set. Additionally, we show how inference, hidden states decoding and parameter learning must be adapted to fit the proposed model. Finally, we run experiments with synthetic and real data to show the capabilities of this new model.
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Keywords

AlgorithmAlgorithmsAuto-regressiveAutoregressionAutoregressiveBayes methodsBayesian networksContinuous variablesData modelsGraphical modelsHidden markov modelsIndependenceInferenceInference modelsInformation asymmetriesLatent variableLinear gaussianMarkov chainMarkov chainsMarkov processesMathematical modelModel selectionNormal distributionParameter learningPenalized likelihoodPredictionProbabilistic logicRegression analysisStructure learningSynthetic and real dataTime seriesTime-seriesTrellis codesYule-walker equations

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position 2/275, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Electrical & Electronic. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-07:

  • Google Scholar: 10
  • WoS: 8
  • Scopus: 11
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-07:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 11.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 11 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/72670/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 216
  • Downloads: 45
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (PUERTO SANTANA, CARLOS ESTEBAN) and Last Author (BIELZA LOZOYA, MARIA CONCEPCION).

the author responsible for correspondence tasks has been PUERTO SANTANA, CARLOS ESTEBAN.

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Awards linked to the item

This work was supported by the Spanish Centre for the Development of Industrial Technology (CDTI) through the IDI-20180156 LearnIIoT project, in part by the Spanish Ministry of Science, Innovation and Universities through the PID2019-109247GB-I00 and RTC2019-006871-7 DSTREAMS Project, and from the project BAYES-CLIMA-NEURO, BBVA Foundation's Grant (2019). The authors would like to thank Aingura IIoT for its support related to filtering the datasets to perform the corresponding experiments in the case of ball-bearing degradation case.
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