Search results for "CLUSTER ANALYSIS"
showing 10 items of 848 documents
Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination
2015
This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive…
Proanthocyanidins and Where to Find Them: A Meta-Analytic Approach to Investigate Their Chemistry, Biosynthesis, Distribution, and Effect on Human He…
2021
Proanthocyanidins (PACs) are a class of polyphenolic compounds that are attracting considerable interest in the nutraceutical field due to their potential health benefits. However, knowledge about the chemistry, biosynthesis, and distribution of PACs is limited. This review summarizes the main chemical characteristics and biosynthetic pathways and the main analytical methods aimed at their identification and quantification in raw plant matrices. Furthermore, meta-analytic approaches were used to identify the main plant sources in which PACs were contained and to investigate their potential effect on human health. In particular, a cluster analysis identified PACs in 35 different plant famili…
A fully automatic method for biological target volume segmentation of brain metastases
2016
Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of…
Integration of HVSR measures and stratigraphic constraints for seismic microzonation studies: the case of Oliveri (ME)
2014
Abstract. Because of its high seismic hazard the urban area of Oliveri has been subject of first level seismic microzonation. The town develops on a large coastal plain made of mixed fluvial/marine sediments, overlapping a complexly deformed substrate. In order to identify points on the area probably suffering relevant site effects and define a preliminary Vs subsurface model for the first level of microzonation, we performed 23 HVSR measurements. A clustering technique of continuous signals has been used to optimize the calculation of the HVSR curves. 42 reliable peaks of the H/V spectra in the frequency range 0.6–10 Hz have been identified. A second clustering technique has been applied t…
Pattern Classification from Multi-beam Acoustic Data Acquired in Kongsfjorden
2021
Climate change is causing a structural change in Arctic ecosystems, decreasing the effectiveness that the polar regions have in cooling water masses, with inevitable repercussions on the climate and with an impact on marine biodiversity. The Svalbard islands under study are an area greatly influenced by Atlantic waters. This area is undergoing changes that are modifying the composition and distribution of the species present. The aim of this work is to provide a method for the classification of acoustic patterns acquired in the Kongsfjorden, Svalbard, Arctic Circle using multibeam technology. Therefore the general objective is the implementation of a methodology useful for identifying the a…
Multi-modal Image Registration Using Fuzzy Kernel Regression
2009
This paper presents a study aimed to the realization of a novel multiresolution registration framework. The transformation function is computed iteratively as a composition of local deformations determined by the maximization of mutual information. At each iteration, local transformations are joint together using fuzzy kernel regression. This technique represents the core of the mothod and it's formally described from a probabilistic perspective. It avoids blocking artifacts and allows to keep the final deformation spatially congruent and smooth. Both qualitative and quantitative experimental results show that this approach is equally effective for registering datasets acquired from both si…
Selecting significant respondents from large audience datasets: The case of the World Hobbit Project
2016
International projects, online questionnaires, or data mining techniques now allow audience researchers to gather very large and complex datasets. But whilst data collection capacity is hugely growing, qualitative analysis, conversely, becomes increasingly difficult to conduct. In this paper, I suggest a strategy that might allow the researcher to manage this complexity. The World Hobbit Project dataset (36,109 cases), including answers to both closed and open-ended questions, was used for this purpose. The strategy proposed here is based on between-methods sequential triangulation, and tries to combine statistical techniques (k-means clustering) with textual analysis. K-means clustering pe…
CHOOSING OF OPTIMAL REFERENCE SAMPLES FOR BOREAL LAKE CHLOROPHYLL A CONCENTRATION MODELING USING AERIAL HYPERSPECTRAL DATA
2018
Abstract. Optical remote sensing has potential to overcome the limitations of point estimations of lake water quality by providing spatial and temporal information. In open ocean waters the optical properties are dominated by phytoplankton density, while the relationship between color and the constituents is more complicated in inland waters varying regionally and seasonally. Concerning the difficulties relating to comprehensive modeling of complex inland and coastal waters, the alternative approach is considered in this paper: the raw digital numbers (DN) recorded using aerial remote hyperspectral sensing are used without corrections and derived by means of regression modeling to predict C…
CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS
2018
Abstract. Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-for…
On the Location Attractiveness of Emerging Countries for Foreign Direct Investments
2016
Our paper investigates the FDI attracting potential of emerging markets by in terms of their location attributes. We use Statistical cluster analysis to study the dynamic evolution of emerging markets’ clusters, based on country attributes that are relevant for the MNEs location decision. We find that countries tend to be grouped at a geographical level or depending on the various resources they possess, except for China that clusters independently. Also, there are numerous countries’ transitions from one cluster to another over the years, which indicate a natural process of changing location attributes and market development for many emerging economies.