scholarly journals Multigrid Accelerated Tensor Approximation of Function Related Multidimensional Arrays

2009 ◽  
Vol 31 (4) ◽  
pp. 3002-3026 ◽  
Author(s):  
B. N. Khoromskij ◽  
V. Khoromskaia
Keyword(s):  
Multidimensional Arrays ◽  
Tensor Approximation ◽  
Approximation Of Function

scholarly journals Computation of low-rank tensor approximation under existence constraint via a forward-backward algorithm

2021 ◽  
pp. 108178
Author(s):  
Marouane Nazih ◽  
Khalid Minaoui ◽  
Elaheh Sobhani ◽  
Pierre Comon
Keyword(s):  
Low Rank ◽  
Rank Tensor ◽  
Tensor Approximation

scholarly journals PRIVACY PRESERVING CLUSTERING BASED ON LINEAR APPROXIMATION OF FUNCTION

2013 ◽  
Vol 12 (5) ◽  
pp. 3443-3451
Author(s):  
Rajesh Pasupuleti ◽  
Narsimha Gugulothu
Keyword(s):  
Linear Approximation ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Privacy Preserving ◽  
Distance Measures ◽  
Clustering Methods ◽  
Sensitive Data ◽  
Processing Information ◽  
Data Objects ◽  
Approximation Of Function

Clustering analysis initiatives  a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of the  requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected by  user.  In this paper, we proposed new clustering method based on linear approximation of function by getting over all idea of behavior knowledge of clustering function, then pick the initial seeds of clusters as the points on linear approximation line and perform clustering operations, unlike grouping data objects into clusters by using distance measures, similarity measures and statistical distributions in traditional clustering methods. We have shown experimental results as clusters based on linear approximation yields good  results in practice with an example of  business data are provided.  It also  explains privacy preserving clusters of sensitive data objects.

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