Search results for "Data Matrix"
showing 6 items of 6 documents
Spectral density of the correlation matrix of factor models: a random matrix theory approach.
2005
We studied the eigenvalue spectral density of the correlation matrix of factor models of multivariate time series. By making use of the random matrix theory, we analytically quantified the effect of statistical uncertainty on the spectral density due to the finiteness of the sample. We considered a broad range of models, ranging from one-factor models to hierarchical multifactor models.
Use of an artificial model of monitoring data to aid interpretation of principal component analysis
2000
Abstract An artificial data matrix of element concentrations at sampling locations was created which included six simulated gradients of correlated variables (Ca+Mg, Ni+V, Pb+Cu+Zn, Cd, Fe and K), representing a simplified model of a National survey. The data matrix model was used to explore the efficiency with which Principal Components Analysis (PCA), without and with Varimax rotation, could derive the imposed gradients. The dependence of PCA on outliers was decreased by log-transformation of data. The Components derived from non-rotated PCA were confounded by bipolar clusters and oblique gradients, both resulting in superimposition of two independent gradients on one Component. Therefore…
Molecular data supports the inclusion of <i>Ildobates neboti</i> Español in Zuphiini (Coleoptera: Carabidae: Harpalinae)
2006
The phylogenetic relationships of Ildobates neboti Español (Coleoptera: Carabidae: Harpalinae) were investigated based on three nuclear genes (full 18S rRNA, and a fragment of each 28S rRNAand wingless).We compiled a data set using published sequences of 32 members of Harpalinae including one example each of Dryptini (genus Desera), Galeritini (Galerita) and Zuphiini (Thalpius), plus three Brachininae as outgroups. These three tribes form the “Dryptitae”, within which various relationships of Ildobates had been proposed. The analyses of the datamatrix using parsimony (with equally weighted and reweighted characters) and Bayesian posterior probabilities all support the monophyly of the three…
Matrix Shuffle- Exchange Networks for Hard 2D Tasks
2021
Convolutional neural networks have become the main tools for processing two-dimensional data. They work well for images, yet convolutions have a limited receptive field that prevents its applications to more complex 2D tasks. We propose a new neural model, called Matrix Shuffle-Exchange network, that can efficiently exploit long-range dependencies in 2D data and has comparable speed to a convolutional neural network. It is derived from Neural Shuffle-Exchange network and has O(log N) layers and O(N ^ 2 log N) total time and O(N^2) space complexity for processing a NxN data matrix. We show that the Matrix Shuffle-Exchange network is well-suited for algorithmic and logical reasoning tasks on …
A statistical approach towards a regionalization of daily rainfall in Sri Lanka
1993
Regionalization of daily rainfall in Sri Lanka was examined using orthogonal factor analysis (OFA) based on daily rainfall data of 42 stations for a 15-year period (1971–1985). The number of potential rainy days was computed from the original data matrix and subjected to S-mode OFA. The first 10 orthogonal factors were shown as highly significant, explaining 65.1 per cent of the total variance of the whole data matrix, where the level of eigenvalues represented was > 1.0. Noticeably, the 10 orthogonal factors clearly revealed the different homogeneous daily rainfall regions in Sri Lanka (labelled as A to J), according to the orthogonal factor high loadings matrix. Delimitation of the daily …
Towards unsupervised analysis of second-order chromatographic data: automated selection of number of components in multivariate curve-resolution meth…
2007
A method to apply multivariate curve-resolution unattendedly is presented. The algorithm is suitable to perform deconvolution of two-way data (e.g. retrieving the individual elution profiles and spectra of co-eluting compounds from signals obtained from a chromatograph equipped with multiple-channel detection: LC-DAD or GC-MS). The method is especially adequate to achieve the advantages of deconvolution approaches when huge amounts of data are present and manual application of multivariate techniques is too time-consuming. The philosophy of the algorithm is to mimic the reactions of an expert user when applying the orthogonal projection approach--multivariate curve-resolution techniques. Ba…