Examinando por Autor "Martino, Luca"

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An Exhaustive Variable Selection Study for Linear Models of Soundscape Emotions: Rankings and Gibbs Analysis
(IEEE, 2022-07-20) San Millán-Castillo, Roberto; Martino, Luca; Morgado, Eduardo; LLorente, Fernando
In the last decade, soundscapes have become one of the most active topics in Acoustics, providing a holistic approach to the acoustic environment, which involves human perception and context. Soundscapes-elicited emotions are central and substantially subtle and unnoticed (compared to speech or music). Currently, soundscape emotion recognition is a very active topic in the literature. We provide an exhaustive variable selection study (i.e., a selection of the soundscapes indicators) to a well-known dataset (emo-soundscapes). We consider linear soundscape emotion models for two soundscapes descriptors: arousal and valence. Several ranking schemes and procedures for selecting the number of variables are applied. We have also performed an alternating optimization scheme for obtaining the best sequences keeping fixed a certain number of features. Furthermore, we have designed a novel technique based on Gibbs sampling, which provides a more complete and clear view of the relevance of each variable. Finally, we have also compared our results with the analysis obtained by the classical methods based on p-values. As a result of our study, we suggest two simple and parsimonious linear models of only 7 and 16 variables (within the 122 possible features) for the two outputs (arousal and valence), respectively. The suggested linear models provide very good and competitive performance, with R2 > 0.86 and R2 > 0.63 (values obtained after a cross-validation procedure), respectively.
An index of effective number of variables for uncertainty and reliability analysis in model selection problems
(Elsevier, 2024-10-16) Martino, Luca; Morgado, Eduardo; San Millán Castillo, Roberto
An index of an effective number of variables (ENV) is introduced for model selection in nested models. This is the case, for instance, when we have to decide the order of a polynomial function or the number of bases in a nonlinear regression, choose the number of clusters in a clustering problem, or the number of features in a variable selection application (to name few examples). It is inspired by the idea of the maximum area under the curve (AUC). The interpretation of the ENV index is identical to the effective sample size (ESS) indices concerning a set of samples. The ENV index improves drawbacks of the elbow detectors described in the literature and introduces different confidence measures of the proposed solution. These novel measures can be also employed jointly with the use of different information criteria, such as the well-known AIC and BIC, or any other model selection procedures. Comparisons with classical and recent schemes are provided in different experiments involving real datasets. Related Matlab code is given
MCMC-driven importance samplers
(Elsevier, 2022) Llorente, Fernando; Curbelo, Ernesto; Martino, Luca; Elvira, V.; Delgado, D.
Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we focus on the class of layered adaptive importance sampling algorithms, which is a family of adaptive importance samplers where Markov chain Monte Carlo algorithms are employed to drive an underlying multiple importance sampling scheme. The modular nature of the layered adaptive importance sampling scheme allows for different possible implementations, yielding a variety of different performances and computational costs. In this work, we propose different enhancements of the classical layered adaptive importance sampling setting in order to increase the efficiency and reduce the computational cost, of both upper and lower layers. The different variants address computational challenges arising in real-world applications, for instance with highly concentrated posterior distributions. Furthermore, we introduce different strategies for designing cheaper schemes, for instance, recycling samples generated in the upper layer and using them in the final estimators in the lower layer. Different numerical experiments show the benefits of the proposed schemes, comparing with benchmark methods presented in the literature, and in several challenging scenarios.
Positive correlational shift between crevicular antimicrobial peptide LL-37, pain and periodontal status following non-surgical periodontal therapy. A pilot study
(2023-05-28) Madruga González, David; Martínez García, Miguel Ángel; Martino, Luca; Hassan, Haidar; Elayat, Ghada; Ghali, Lucy; Ceballos García, Laura
Background. Periodontitis has a high prevalence and uncertain recurrence. Unlike the pro-inflammatory cytokine profile, little is known about the anti-inflammatory cytokine and antimicrobial peptide overview following treatment. The present study aimed to evaluate if any of the antimicrobial peptide LL-37, interleukin (IL) 4, 10 and 6 together with the volume of gingival crevicular fluid (GCF) and total protein concentration in GCF could be used as correlative biomarkers for the severity in periodontitis as well as prognostic factors in the management of the disease. Methods. Forty-five participants were recruited and allocated to the healthy (15), Stage I-II (15) or Stage III-IV periodontitis (15) group. Along with periodontal examination, GCF samples were obtained at baseline and 4-6 weeks following scaling and root planing (SRP) for the periodontitis groups. GCF samples were analyzed by ELISA kits to quantify LL-37 and IL-4, -6 and - 10. One-way ANOVA followed by Dunnett's test was used to determine differences among the three groups at baseline. Two-way ANOVA followed by Sidak's post-hoc test was used to compare between pre- and post-SRP in the two periodontitis groups. Results. The amount of GCF volume was significantly correlated to the severity of periodontitis and decreased following SRP, particularly in the Stage III-IV group (p < 0.01). The levels of LL-37, IL-6, and pain and periodontal clinical parameters were significantly correlated to the severity of periodontitis. IL-4 and IL-10 in the periodontitis groups were significantly lower than the healthy group (p < 0.0001) and barely improved following SRP up to the level of the healthy group. Conclusions. With the limitations of this study, crevicular LL-37 may be a candidate for a biomarker of periodontitis and the associated pain upon probing.
Spectral information criterion for automatic elbow detection
(Elsevier, 2023) Martino, Luca; San Millán-Castillo, Roberto; Morgado, Eduardo
We introduce a generalized information criterion that contains other well-known information criteria, such as Bayesian information Criterion (BIC) and Akaike information criterion (AIC), as special cases. Furthermore, the proposed spectral information criterion (SIC) is also more general than the other information criteria, e.g., since the knowledge of a likelihood function is not strictly required. SIC extracts geometric features of the error curve and, as a consequence, it can be considered an automatic elbow detector. SIC provides a subset of all possible models, with a cardinality that often is much smaller than the total number of possible models. The elements of this subset are ‘‘elbows’’ of the error curve. A practical rule for selecting a unique model within the sets of elbows is suggested as well. Theoretical invariance properties of SIC are analyzed. Moreover, we test SIC in ideal scenarios where provides always the optimal expected results. We also test SIC in several numerical experiments: some involving synthetic data, and two experiments involving real datasets. They are all real-world applications such as clustering, variable selection, or polynomial order selection, to name a few. The results show the benefits of the proposed scheme. Matlab code related to the experiments is also provided. Possible future research lines are finally discussed.
Universal and automatic elbow detection for learning the effective number of components in model selection problems
(Elsevier, 2023) Morgado, Eduardo; Martino, Luca; San Millán-Castillo, Roberto
We design a Universal Automatic Elbow Detector (UAED) for deciding the effective number of components in model selection problems. The relationship with the information criteria widely employed in the literature is also discussed. The proposed UAED does not require the knowledge of a likelihood function and can be easily applied in diverse applications, such as regression and classification, feature and/or order selection, clustering, and dimension reduction. Several experiments involving synthetic and real data show the advantages of the proposed scheme with benchmark techniques in the literature.