Search results for "sparse"
showing 10 items of 75 documents
Nonlinear data description with Principal Polynomial Analysis
2012
Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Performance of PCA is however hampered when data exhibits nonlinear feature relations. In this work, we propose a new framework for manifold learning based on the use of a sequence of Principal Polynomials that capture the eventually nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) is shown to generalize PCA. Unlike recently proposed nonlinear methods (e.g. spectral/kernel methods and projection pursuit techniques, neural networks), PPA features are easily interpretable and the method leads to a fully invertible transform, which is a desirable property…
Fusion of CNN and sparse representation for threat estimation near power lines and poles infrastructure using aerial stereo imagery
2021
Abstract Fires or electrical hazards and accidents can occur if vegetation is not controlled or cleared around overhead power lines, resulting in serious risks to people and property and significant costs to the community. There are numerous blackouts due to interfering the trees with the power transmission lines in hilly and urban areas. Power distribution companies are facing a challenge to monitor the vegetation to avoid blackouts and flash-over threats. Recently, several methods have been developed for vegetation monitoring; however, existing methods are either not accurate or could not provide better disparity map in the textureless region. Moreover, are not able to handle depth discon…
Feature extraction from remote sensing data using Kernel Orthonormalized PLS
2007
This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called kernel orthonormalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability.
Estimation of sparse generalized linear models: the dglars package
2013
dglars is a public available R package that implements the method proposed in Augugliaro, Mineo and Wit (2013) developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method (LARS). The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve; specifically a predictor-corrector algorithm and a cyclic coordinate descent algorithm.
Directionlets: Anisotropic Multidirectional representation with separable filtering
2006
In spite of the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency of its representation is limited by the spatial isotropy of its basis functions built in the horizontal and vertical directions. One-dimensional (1-D) discontinuities in images (edges and contours) that are very important elements in visual perception, intersect too many wavelet basis functions and lead to a nonsparse representation. To efficiently capture these anisotropic geometrical structures characterized by many more than the horizontal and vertical directions, a more complex multidirectional (M-DIR) and anisotropic transform is required. We present a new lattice-based pe…
Automatic knowledge discovery from sparse and large-scale educational data : case Finland
2017
The Finnish educational system has received a lot of attention during the 21st century. Especially, the outstanding results in the first three cycles of the Programme for International Student Assessment (PISA) have made Finland’s education system internationally famous, and its unique characteristics have been under active research by various, predominantly educational, scholars since then. However, despite the availability of real but often sparse big data sets that would allow more evidence-based decision making, existing research to date has mostly concentrated on using classical qualitative and (univariate) quantitative methods. This thesis discusses, in general terms, knowledge discove…
Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition
2018
Tensor decomposition has been widely employed for EEG signal processing in recent years. Constrained and regularized tensor decomposition often attains more meaningful and interpretable results. In this study, we applied sparse nonnegative CANDECOMP/PARAFAC tensor decomposition to ongoing EEG data under naturalistic music stimulus. Interesting temporal, spectral and spatial components highly related with music features were extracted. We explored the ongoing EEG decomposition results and properties in a wide range of sparsity levels, and proposed a paradigm to select reasonable sparsity regularization parameters. The stability of interesting components extraction from fourteen subjects’ dat…
Applicability of a displaced-beam laser scintillometer in a sparse tall Mediterranean vegetation
2009
Recent studies showed that the sensible heat flux (H) measured with an array of eddy-correlation system has an high spatial and temporal variability over sparse tall vegetation, such as olive trees, whereas H determined with a displaced-beam laser scintillometer (DBLS) appeared to behave more stable. In this study, the results are shown of two field experiments performed over an olive tree plantation in Sicily in 2007 and 2008, in order to investigate the applicability of a DBSL in combination with remote sensing techniques for the actual evapotranspiration assessment. In 2007 the laser beams was closer to the top of the canopy than in 2008. Various aspects of the scintillation method will …
Association of child maltreatment subtypes and long-term physical health in a German representative sample
2018
ABSTRACT Background: Child maltreatment is a major public problem, associated with enormous consequences on the individual and socioeconomic level. Studies show a clear impact of child maltreatment on long-term physical health. However, there is a lack of analyses comprising a wide variety of subtypes of maltreatment and addressing cumulative effects of different maltreatment subtypes experienced during childhood on physical health. Objective: The objective of this analysis was to assess the association of different subtypes and the intensity of child maltreatment with long-term physical health outcomes. Methods: In a cross-sectional observational approach, a representative sample of the Ge…
Estimating aggregated nutrient fluxes in four Finnish rivers via Gaussian state space models
2013
Reliable estimates of the nutrient fluxes carried by rivers from land-based sources to the sea are needed for efficient abatement of marine eutrophication. Although nutrient concentrations in rivers generally display large temporal variation, sampling and analysis for nutrients, unlike flow measurements, are rarely performed on a daily basis. The infrequent data calls for ways to reliably estimate the nutrient concentrations of the missing days. Here, we use the Gaussian state space models with daily water flow as a predictor variable to predict missing nutrient concentrations for four agriculturally impacted Finnish rivers. Via simulation of Gaussian state space models, we are able to esti…