2012年
Fast Similarity Computation in Factorized Tensors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- ,
- ,
- 巻
- 7404
- 号
- 開始ページ
- 226
- 終了ページ
- 239
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1007/978-3-642-32153-5_16
- 出版者・発行元
- Springer
Low-rank factorizations of higher-order tensors have become an invaluable tool for researchers from many scientific disciplines. Tensor factorizations have been successfully applied for moderately sized multimodal data sets involving a small number of modes. However, a significant hindrance to the full realization of the potential of tensor methods is a lack of scalability on the client side: even when low-rank representations are provided by an external agent possessing the necessary computational resources, client applications are quickly rendered infeasible by the space requirements for explicitly storing a (dense) low-rank representation of the input tensor. We consider the problem of efficiently computing common similarity measures between entities expressed by fibers (vectors) or slices (matrices) within a given factorized tensor. We show that after appropriate preprocessing, inner products can be efficiently computed independently of the dimensions of the input tensor. © 2012 Springer-Verlag.
- リンク情報
- ID情報
-
- DOI : 10.1007/978-3-642-32153-5_16
- ISSN : 0302-9743
- ISSN : 1611-3349
- DBLP ID : conf/sisap/HouleKN12
- SCOPUS ID : 84865517005