A Parallel Nonnegative Tensor Factorization Algorithm for Mining Global Climate Data

Zhang, Qiang; Berry, Michael W.; Lamb, Brian; Samuel, Tabitha K.

doi:10.1007/978-3-642-01973-9_45

Cited by 24 publications

(16 citation statements)

References 13 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1. NTF factorizes a tensor X into factor matrices T , U , V been a lot of NTF researches concerning sparse constraints [6], [17] and acceleration techniques [7], [18]. As explained, data sparsity becomes a serious problem for large or high order tensors.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Non-negative Multiple Tensor Factorization

Takeuchi

Tomioka

Ishiguro

et al. 2013

2013 IEEE 13th International Conference on Data Mining

View full text Add to dashboard Cite

Abstract-Non-negative Tensor Factorization (NTF) is a widely used technique for decomposing a non-negative value tensor into sparse and reasonably interpretable factors. However, NTF performs poorly when the tensor is extremely sparse, which is often the case with real-world data and higher-order tensors. In this paper, we propose Non-negative Multiple Tensor Factorization (NMTF), which factorizes the target tensor and auxiliary tensors simultaneously. Auxiliary data tensors compensate for the sparseness of the target data tensor. The factors of the auxiliary tensors also allow us to examine the target data from several different aspects. We experimentally confirm that NMTF performs better than NTF in terms of reconstructing the given data. Furthermore, we demonstrate that the proposed NMTF can successfully extract spatio-temporal patterns of people's daily life such as leisure, drinking, and shopping activity by analyzing several tensors extracted from online review data sets.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The non-negativity constraints yield sparse and reasonably interpretable factorization results [3]. NTF has been applied and preforms well in various fields [4]- [7]. An advantage of tensor data over conventionally studied matrix data is its ability to represent observations with various attributes.…”

Section: Introductionmentioning

confidence: 99%

Non-negative Multiple Tensor Factorization

Takeuchi

Tomioka

Ishiguro

et al. 2013

2013 IEEE 13th International Conference on Data Mining

View full text Add to dashboard Cite

show abstract

“…Then the factors for the complete tensor are estimated from these sub-factors, in parallel, using special update rules. [27] introduces various parallelization strategies for speeding up factor matrix update step in alternating least squares (ALS) based decomposition. Authors propose techniques for distributing a large tensor onto the servers in a cluster, minimizing data exchange, and limiting the memory needed for storing matrices or tensors.…”

Section: Related Workmentioning

confidence: 99%

Focusing Decomposition Accuracy by Personalizing Tensor Decomposition (PTD)

Huang

Candan

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

View full text Add to dashboard Cite

Tensor decomposition operation is the basis for many data analysis tasks from clustering, trend detection, anomaly detection, to correlation analysis. One key problem with tensor decomposition, however, is its computational complexity -especially for dense data sets, the decomposition process takes exponential time in the number of tensor modes; the process is relatively faster for sparse tensors, but decomposition is still a major bottleneck in many applications. While it is possible to reduce the decomposition time by trading performance with decomposition accuracy, a drop in accuracy may not always be acceptable. In this paper, we first recognize that in many applications, the user may have a focus of interest -i.e., part of the data for which the user needs high accuracy-and beyond this area focus, accuracy may not be as critical. Relying on this observation, we propose a novel Personalized Tensor Decomposition (PTD) mechanism for accounting for the user's focus: PTD takes as input one or more areas of focus and performs the decomposition in such a way that, when reconstructed, the accuracy of the tensor is boosted for these areas of focus. We discuss alternative ways PTD can be implemented. Experiments show that PTD helps boost accuracy at the foci of interest, while reducing the overall tensor decomposition time.

show abstract

“…In [17] and [23] we find two interesting approaches, where a tensor is viewed as a stream and the challenge is to track the decomposition. In terms of parallel algorithms, [26] introduces a parallel Non-negative Tensor Factorization. Finally, [24,22] propose randomized, sampling based Tucker3 decompositions.…”

Section: Related Workmentioning

confidence: 99%

ParCube: Sparse Parallelizable Tensor Decompositions

Papalexakis

Faloutsos

Sidiropoulos

2012

Machine Learning and Knowledge Discovery in Databases

127

View full text Add to dashboard Cite

Abstract. How can we efficiently decompose a tensor into sparse factors, when the data does not fit in memory? Tensor decompositions have gained a steadily increasing popularity in data mining applications, however the current state-of-art decomposition algorithms operate on main memory and do not scale to truly large datasets. In this work, we propose ParCube, a new and highly parallelizable method for speeding up tensor decompositions that is well-suited to producing sparse approximations. Experiments with even moderately large data indicate over 90% sparser outputs and 14 times faster execution, with approximation error close to the current state of the art irrespective of computation and memory requirements. We provide theoretical guarantees for the algorithm's correctness and we experimentally validate our claims through extensive experiments, including four different real world datasets (Enron, Lbnl, Facebook and Nell), demonstrating its effectiveness for data mining practitioners. In particular, we are the first to analyze the very large Nell dataset using a sparse tensor decomposition, demonstrating that ParCube enables us to handle effectively and efficiently very large datasets.

show abstract

A Parallel Nonnegative Tensor Factorization Algorithm for Mining Global Climate Data

Cited by 24 publications

References 13 publications

Non-negative Multiple Tensor Factorization

Non-negative Multiple Tensor Factorization

Focusing Decomposition Accuracy by Personalizing Tensor Decomposition (PTD)

ParCube: Sparse Parallelizable Tensor Decompositions

Contact Info

Product

Resources

About