Paper Info
Title
Change Detection In Multidimensional Data Streams With Efficient Tensor Subspace Model
Abstract
The paper presents a method for change detection in multidimensional streams of data based on a tensor model constructed from the Higher-Order Singular Value Decomposition of raw data tensors. The method was applied to the problem of video shot detection showing good accuracy and high speed of execution compared with other more time demanding tensor models. In this paper we show two efficient algorithms for tensor model construction and tensor model update from the stream of data.
Year
DOI
Venue
2018
10.1007/978-3-319-92639-1_58
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS (HAIS 2018)
Keywords
Field
DocType
Tensor change detection, Video shot detection, Orthogonal tensor space, Higher-Order Singular Value Decomposition
Singular value decomposition,Data stream mining,Change detection,Pattern recognition,Subspace topology,Tensor,Computer science,Raw data,Artificial intelligence,Higher-order singular value decomposition
Conference
Volume
ISSN
Citations 
10870
0302-9743
1
PageRank 
References 
Authors
0.37
14
1
Name
Order
Citations
PageRank
Boguslaw Cyganek114524.53