Results 231 to 240 of about 3,384 (256)

On Fast algorithms for orthogonal Tucker decomposition

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
We propose algorithms for Tucker tensor decomposition, which can avoid computing singular value decomposition or eigenvalue decomposition of large matrices as in the work-horse higher order orthogonal iteration (HOOI) algorithm. The novel algorithms require computational cost of O(I 3 R), which is cheaper than O(I 3 R + IR 4 + R 6 ) of HOOI for ...
Anh Huy Phan 0001, Andrzej Cichocki, Petr Tichavský   +2 more
openaire   +1 more source

Nonnegative Tucker decomposition with alpha-divergence

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
Nonnegative tucker decomposition (NTD) is a recent multiway extension of nonnegative matrix factorization (NMF), where nonnega- tivity constraints are incorporated into Tucker model. In this paper we consider alpha-divergence as a discrepancy measure and derive multiplicative updating algorithms for NTD.
Yong-Deok Kim, Andrzej Cichocki, Seungjin Choi   +2 more
openaire   +1 more source

Parallel Tucker Decomposition with Numerically Accurate SVD

50th International Conference on Parallel Processing, 2021
Tucker decomposition is a low-rank tensor approximation that generalizes a truncated matrix singular value decomposition (SVD). Existing parallel software has shown that Tucker decomposition is particularly effective at compressing terabyte-sized multidimensional scientific simulation datasets, computing reduced representations that satisfy a specified
Zitong Li, Qiming Fang, Grey Ballard
openaire   +1 more source

On optimizing distributed non-negative Tucker decomposition

Proceedings of the ACM International Conference on Supercomputing, 2019
The Tucker decomposition generalizes singular value decomposition (SVD) to high dimensional tensors. It factorizes a given N-dimensional tensor as the product of a small core tensor and a set of N factor matrices. Non-negative Tucker Decomposition (NTD) is a variant that imposes the constraint that the entries of the core and the factor matrices must ...
Venkatesan T. Chakaravarthy, Shivmaran S. Pandian, Saurabh Raje, Yogish Sabharwal   +3 more
openaire   +1 more source

An Efficient Randomized Algorithm for Computing the Approximate Tucker Decomposition

Journal of Scientific Computing, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maolin Che, Yimin Wei 0001, Hong Yan 0001   +2 more
openaire   +1 more source

Double‐Tucker Decomposition and Its Computations

Numerical Linear Algebra with Applications
ABSTRACTThe famous Tucker decomposition has been widely and successfully used in many fields. However, it often suffers from the curse of dimensionality due to the core tensor and large ranks. To tackle this issue, we introduce an additional core tensor into Tucker decomposition and propose the so‐called double‐Tucker (dTucker) decomposition.
Mengyu Wang, Honghua Cui, Hanyu Li
openaire   +1 more source

Supervised Nonnegative Tucker Decomposition for Computational Phenotyping

2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019
With the availability of Electronic Health Records (EHR) data, lots of predictive tasks in medical practice seem solvable by building predictive models. However, EHR data always contains various medical concepts (e.g., diagnosis, medicines, lab tests) with high dimensions and mass correlations among them.
Kai Yang, Wenqi Sun, Junfeng Zhao 0001, Yasha Wang, Bing Xie   +4 more
openaire   +1 more source

Wafer Pattern Recognition Using Tucker Decomposition

2019 IEEE 37th VLSI Test Symposium (VTS), 2019
In production test data analytics, it is often that an analysis involves the recognition of a conceptual pattern on a wafer map. A wafer pattern may hint a particular issue in the production by itself or guide the analysis into a certain direction. In this work, we introduce a novel approach to recognize patterns on a wafer map of pass/fail locations ...
Ahmed Wahba, Li-C. Wang, Zheng Zhang 0005, Nik Sumikawa   +3 more
openaire   +1 more source

Quality assessment for color images with tucker decomposition

2012 19th IEEE International Conference on Image Processing, 2012
As an extension of the singular value decomposition based approaches, a novel metric based on Tucker decomposition for color image quality assessment is proposed in this paper. It extracts both the spacial and chromatic information of a color image with Tucker decomposition.
Cheng Cheng, Hanli Wang
openaire   +1 more source

Fast and Efficient Algorithms for Nonnegative Tucker Decomposition

2008
In this paper, we propose new and efficient algorithms for nonnegative Tucker decomposition (NTD): Fast i¾?-NTD algorithm which is much precise and faster than i¾?-NTD [1]; and β-NTD algorithm based on the βdivergence. These new algorithms include efficient normalization and initialization steps which help to reduce considerably the running time and ...
Anh Huy Phan 0001, Andrzej Cichocki
openaire   +1 more source