Tensor decompositionMulti-dimensional tensor data have gained increasing attention in the recent years, especially in biomedical imaging analyses. However, the most existing tensor models are only based on the mean information of imaging pixels. Motivated by multimodal optical imaging data in a breast ...
In a second part, we show that the use of a tensor space–time coding (TSTC) structure at both the source node and the relay node of a one-way two-hop multi-input multi-output (MIMO) relay communication system leads to a nested Tucker decomposition of the fourth-order tensor formed ...
1, is a decomposition of Y as a linear combination of a minimal number of rank-1 tensors:Y=∑r=1Rhr∘sr∘ar,where hr, sr, ar are the rth columns of matrices H∈CI×R, S∈CJ×R and A∈CK×R. This trilinear model was independently introduced in psychometrics [2] and ...
Cichocki, Andrzej. "Era of big data processing: A new approach via tensor networks and tensor decompositions." arXiv preprint arXiv:1403.2048 (2014).(Paper) Tensor decomposition and data fusion Lahat, Dana, Tülay Adali, and Christian Jutten. "Multimodal data fusion: an overview of methods, ch...
Accordingly, we present PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition (PREDICTD), which treats the imputation problem as a tensor completion task, and employs a parallelized algorithm based on the PARAFAC/CANDECOMP method8,9,]. Our implementation, developed on consumer cloud...
1. Tensor Decomposition and its application: http://epubs.siam.org/doi/pdf/10.1137/07070111X 好文要顶 关注我 收藏该文 微信分享 stonestone 粉丝- 2 关注- 0 +加关注 0 0 升级成为会员 « 上一篇: Sparse low rank approximation » 下一篇: Postdoctoral Position ...
. These networks are often large-scale and of high dimensionality. Therefore, dealing with such networks became a challenging task. An intuitive way to deal with this complexity is to resort to tensors. In this context, the application of tensor decomposition has proven its usefulness in ...
TensorFaces: An Application of the Tucker Decomposition • Example: 7942 pixels x 16 illuminations x 11 subjects • PCA (eigenfaces): SVD of 7942 x 176 matrix • Tensorfaces: Tucker-2 decomposition of 7942 x 16 x 11 tensor M.A.O. Vasilescu & D. Terzopoulos, CVPR’03 tensorfaces il...
张量(二):张量分解(tensor decomposition) 与矩阵分解一样,我们希望通过张量分解去提取原数据中所隐藏的信息或主要成分。当前主流的张量分解方法有CP分解,Tucker分解,t-SVD分解等,更新的方法大多是在他们的基础上做进一步的改进或引用。因此,张量分解也是张量算法的基础。下面分别做介绍。 一、CP分解 CP分解是将任意高...
1. Tensor Decomposition and its application: http://epubs.siam.org/doi/pdf/10.1137/07070111X Share this: Facebook X Leave a comment Write a comment... Log in or provide your name and email to leave a comment. Email me new posts Instantly Daily Weekly Email me new comments Save my ...