Search results for "SVD"
showing 4 items of 14 documents
Dimensionality reduction for large spatio-temporal datasets based on SVD
2009
Many models for spatio-temporal measurements Z(s; t) can be written as a sum of a systematic component and a residual component: Z = M + E. The approach presented here incorporates two Singular Value Decompositions (SVD). The first SVD is applied to the space-time data matrix Z with cross-validation to choose the number of smoothed singular vectors to use as temporal basis functions for modelling spatially varying temporal trend in the matrix M. The second SVD is applied to the spatio-temporal matrix E of residuals from the trend models fitted at each site; it represents spatially correlated short time scale temporal processes. The remaining stochastic structure is explained by simple autor…
A spatio-temporal model based on the SVD to analyze large spatio-temporal datasets
2009
A common problem in the analysis of space-time data is to compress a large dataset in order to extract the underlying trends. Empirical orthogonal function (EOF) analysis is a useful tool for examining both the temporal and the spatial variation in atmospherical and physical process and a convenient method of performing this is the Singular Value Decomposition (SVD). Many spatio-temporal models for measurements Z(s; t) at location s at time t, can be written as a sum of a systematic component and a residual component: Z = M+E, where Z, M and E are all T x N matrices. Our approach permits modeling of incomplete data matrices using an "EM-like" iterative algorithm for the SVD. We model the tr…
Part-of-Speech Induction by Singular Value Decomposition and Hierarchical Clustering
2006
Part-of-speech induction involves the automatic discovery of word classes and the assignment of each word of a vocabulary to one or several of these classes. The approach proposed here is based on the analysis of word distributions in a large collection of German newspaper texts. Its main advantage over other attempts is that it combines the hierarchical clustering of context vectors with a previous step of dimensionality reduction that minimizes the effects of sampling errors.
An SVD-Based Approach for Ghost Detection and Removal in High Dynamic Range Images
2012
International audience; In this paper, we propose a simple method for the ghost detection problem in the context of merging multiple low dynamic range (LDR) images to form a high dynamic range (HDR) image. We show that the second biggest singular values extracted over local spatio-temporal neighbourhoods can be effectively used for ghost region detection. Furthermore, we combine the proposed method with an exposure fusion technique to generate final HDR image free of ghosting artefacts. We present experimental results to illustrate the efficiency of the proposed method and quantitative comparison with other existing approaches show the good performance of our method in detecting and removin…