Search results for " dimensionality"
showing 10 items of 129 documents
Synthetic phenomenology and high-dimensional buffer hypothesis
2012
Synthetic phenomenology typically focuses on the analysis of simplified perceptual signals with small or reduced dimensionality. Instead, synthetic phenomenology should be analyzed in terms of perceptual signals with huge dimensionality. Effective phenomenal processes actually exploit the entire richness of the dynamic perceptual signals coming from the retina. The hypothesis of a high-dimensional buffer at the basis of the perception loop that generates the robot synthetic phenomenology is analyzed in terms of a cognitive architecture for robot vision the authors have developed over the years. Despite the obvious computational problems when dealing with high-dimensional vectors, spaces wit…
FDA dimension reduction techniques and components separation in Fourier-transform infrared spectroscopy
2020
FTIR spectroscopy is a measurement technique used to obtain an infrared spectrum of absorption of a solid (or a liquid or a gas), for the characterization of specific chemical components of materials. When repeated measures are taken on samples of materials, the result is a collection of spectra representing a set of samples from continous functions (signals) defined in the domain of the frequencies. An unifying approach to the study of a collection of FTIR spectra is proposed to deal with the presence of random shifts in the peaks, the identification of representative spectra and finally the characterization of the observed differences: in the functional data framework, the performance of …
Multi-temporal and Multi-source Remote Sensing Image Classification by Nonlinear Relative Normalization
2016
Remote sensing image classification exploiting multiple sensors is a very challenging problem: data from different modalities are affected by spectral distortions and mis-alignments of all kinds, and this hampers re-using models built for one image to be used successfully in other scenes. In order to adapt and transfer models across image acquisitions, one must be able to cope with datasets that are not co-registered, acquired under different illumination and atmospheric conditions, by different sensors, and with scarce ground references. Traditionally, methods based on histogram matching have been used. However, they fail when densities have very different shapes or when there is no corres…
Nonlinear Distribution Regression for Remote Sensing Applications
2020
In many remote sensing applications, one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms, such as neural networks, random forests, or the Gaussian processes, are readily available to relate the two. However, we often encounter situations where the target variable is only available at the group level, i.e., collectively associated with a number of remotely sensed observations. This problem setting is known in statistics and machine learning as multiple instance learning (MIL) or distribution regression (DR). This article introduces a nonlinear (kern…
Random Feature Approximation for Online Nonlinear Graph Topology Identification
2021
Online topology estimation of graph-connected time series is challenging, especially since the causal dependencies in many real-world networks are nonlinear. In this paper, we propose a kernel-based algorithm for graph topology estimation. The algorithm uses a Fourier-based Random feature approximation to tackle the curse of dimensionality associated with the kernel representations. Exploiting the fact that the real-world networks often exhibit sparse topologies, we propose a group lasso based optimization framework, which is solve using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. The experiments con…
Multivariate GARCH estimation via a Bregman-proximal trust-region method
2011
The estimation of multivariate GARCH time series models is a difficult task mainly due to the significant overparameterization exhibited by the problem and usually referred to as the "curse of dimensionality". For example, in the case of the VEC family, the number of parameters involved in the model grows as a polynomial of order four on the dimensionality of the problem. Moreover, these parameters are subjected to convoluted nonlinear constraints necessary to ensure, for instance, the existence of stationary solutions and the positive semidefinite character of the conditional covariance matrices used in the model design. So far, this problem has been addressed in the literature only in low…
Model selection in linear mixed-effect models
2019
Linear mixed-effects models are a class of models widely used for analyzing different types of data: longitudinal, clustered and panel data. Many fields, in which a statistical methodology is required, involve the employment of linear mixed models, such as biology, chemistry, medicine, finance and so forth. One of the most important processes, in a statistical analysis, is given by model selection. Hence, since there are a large number of linear mixed model selection procedures available in the literature, a pressing issue is how to identify the best approach to adopt in a specific case. We outline mainly all approaches focusing on the part of the model subject to selection (fixed and/or ra…
Building up adjusted indicators of students' evaluation of university courses using generalized item response models
2012
This article advances a proposal for building up adjusted composite indicators of the quality of university courses from students’ assessments. The flexible framework of Generalized Item Response Models is adopted here for controlling the sources of heterogeneity in the data structure that make evaluations across courses not directly comparable. Specifically, it allows us to: jointly model students’ ratings to the set of items which define the quality of university courses; explicitly consider the dimensionality of the items composing the evaluation form; evaluate and remove the effect of potential confounding factors which may affect students’ evaluation; model the intra-cluster variabilit…
Self-Assembly of Zr(C2O4)44– Metallotectons and Bisimidazolium Cations: Influence of the Dication on H-Bonded Framework Dimensionality and Material P…
2011
Assemblies involving [Zr(C2O4)4]4– metallotectons (C2O42– = oxalate) and linear, flexible, or V-shaped organic cations (H2-Lx)2+ derived from the 1,4-bisimidazol-1-ylbenzene molecule have been envisioned to elaborate porous frameworks based on ionic H-bonds. Five architectures of formula [{(H2-L1)2Zr(C2O4)4}·2H2O] (1), [{(H2-L2)2Zr(C2O4)4}·6H2O] (2), [{(H2-L3)2Zr(C2O4)4}·6H2O] (3), [{(H2-L4)2Zr(C2O4)4}·H2O] (4), and [{(H2-L5)2Zr(C2O4)4}·6H2O] (5) (with L1 = p-bis(imidazol-1-yl)benzene, L2 = p-bis(2-methylimidazol-1-yl)benzene, L3 = p-bis(imidazol-1-yl)-2,5-dimethylbenzene, L4 = p-bis(imidazol-1-ylmethyl)benzene, L5 = m-bis(imidazol-1-yl)benzene) have been obtained; 1–3, and 5 show an open-f…
The Importance of Electronic Dimensionality in Multiorbital Radical Conductors.
2019
The exceptional performance of oxobenzene-bridged bis-1,2,3-dithiazolyls 6 as single-component neutral radical conductors arises from the presence of a low-lying π-lowest unoccupied molecular orbital, which reduces the potential barrier to charge transport and increases the kinetic stabilization energy of the metallic state. As part of ongoing efforts to modify the solid-state structures and transport properties of these so-called multiorbital materials, we report the preparation and characterization of the acetoxy, methoxy, and thiomethyl derivatives 6 (R = OAc, OMe, SMe). The crystal structures are based on ribbonlike arrays of radicals laced together by S···N' and S···O' secondary bondin…