Search results for "sparse"

showing 5 items of 75 documents

Weighted Clustering of Sparse Educational Data

2015

Clustering as an unsupervised technique is predominantly used in unweighted settings. In this paper, we present an efficient version of a robust clustering algorithm for sparse educational data that takes the weights, aligning a sample with the corresponding population, into account. The algorithm is utilized to divide the Finnish student population of PISA 2012 (the latest data from the Programme for International Student Assessment) into groups, according to their attitudes and perceptions towards mathematics, for which one third of the data is missing. Furthermore, necessary modifications of three cluster indices to reveal an appropriate number of groups are proposed and demonstrated. pe…

sparse educational dataPISAclustering
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SparseHC: A Memory-efficient Online Hierarchical Clustering Algorithm

2014

Computing a hierarchical clustering of objects from a pairwise distance matrix is an important algorithmic kernel in computational science. Since the storage of this matrix requires quadratic space with respect to the number of objects, the design of memory-efficient approaches is of high importance to this research area. In this paper, we address this problem by presenting a memory-efficient online hierarchical clustering algorithm called SparseHC. SparseHC scans a sorted and possibly sparse distance matrix chunk-by-chunk. Meanwhile, a dendrogram is built by merging cluster pairs as and when the distance between them is determined to be the smallest among all remaining cluster pairs. The k…

sparse matrixClustering high-dimensional dataTheoretical computer scienceonline algorithmsComputer scienceSingle-linkage clusteringComplete-linkage clusteringNearest-neighbor chain algorithmConsensus clusteringmemory-efficient clusteringCluster analysisk-medians clusteringGeneral Environmental ScienceSparse matrix:Engineering::Computer science and engineering [DRNTU]k-medoidsDendrogramConstrained clusteringHierarchical clusteringDistance matrixCanopy clustering algorithmGeneral Earth and Planetary SciencesFLAME clusteringHierarchical clustering of networkshierarchical clusteringAlgorithmProcedia Computer Science
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Mapping soil water content under sparse vegetation and changeable sky conditions: comparison of two thermal inertia approaches

2013

A critical analysis of a thermal inertia approach to map surface soil water content on bare and sparsely vegetated soils by means of remotely sensed data is reported. The study area is an experimental field located in Barrax, Spain. In situ data were acquired within the Barrax 2011 research project. An advanced hyperspectral scanner airborne imager provides images in the visible/near-infrared and thermal infrared bands. Images were acquired both in day and night times by the Instituto Nacional de Técnica Aeroespacial between 12th and 13th of June 2011. The scene covers a corn irrigation pivot surrounded by bare soil, where a set of in situ data have been collected both previously and simult…

sparse vegetationCloud covermedia_common.quotation_subjectSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaHyperspectral imagingcloudiness.soil water contentVegetationthermal inertiaSoil thermal propertiesSkySoil waterThermographyGeneral Earth and Planetary SciencesEnvironmental scienceSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliWater contentSettore ICAR/06 - Topografia E Cartografiamedia_commonRemote sensing
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Sparse nonnegative tensor decomposition using proximal algorithm and inexact block coordinate descent scheme

2021

Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extracted components are nonnegative and usually sparse. Nevertheless, the sparsity is only a side effect and cannot be explicitly controlled without additional regularization. In this paper, we investigated the nonnegative CANDECOMP/PARAFAC (NCP) decomposition with the sparse regularization item using l1-norm (sparse NCP). When high sparsity is imposed, the factor matrices will contain more zero components and will not be of full column rank. Thus, the sparse NCP is prone to rank deficiency, and the algorithms of sparse NCP may not converge. In this paper, we proposed a novel model of sparse NCP w…

tensor decompositionsignaalinkäsittelyproximal algorithmalgoritmitMathematicsofComputing_NUMERICALANALYSISinexact block coordinate descentsparse regularizationnonnegative CANDECOMP/PARAFAC decomposition
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Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing units

2021

We contribute to the optimization of the sparse matrix-vector product by introducing a variant of the coordinate sparse matrix format that balances the workload distribution and compresses both the indexing arrays and the numerical information. Our approach is multi-platform, in the sense that the realizations for (general-purpose) multicore processors as well as graphics accelerators (GPUs) are built upon common principles, but differ in the implementation details, which are adapted to avoid thread divergence in the GPU case or maximize compression element-wise (i.e., for each matrix entry) for multicore architectures. Our evaluation on the two last generations of NVIDIA GPUs as well as In…

workload balancingMulti-core processorComputer Networks and CommunicationsComputer sciencesparse matrix-vector productParallel computingLoad balancing (computing)coordinate sparse matrix formatSparse matrix vectorcompressionExascale computingComputer Science ApplicationsTheoretical Computer ScienceComputational Theory and MathematicsCompression (functional analysis)Product (mathematics)Graphicsgraphics processing units (GPUs)multicoreprocessors (CPUs)Software
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