Tensors for Data Mining and Data Fusion

Papalexakis, Evangelos E.; Faloutsos, Christos; Sidiropoulos, Nicholas D.

doi:10.1145/2915921

Cited by 250 publications

(35 citation statements)

References 133 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the process of in-depth research, the recommendation system proposes a new form that can represent complex data and heterogeneous information networks, namely, tensors. The low-rank tensor factorization applied to the recommendation system can usually consider the correlation between multidimensional factors, such as users, items, topics, contexts, and so on [27].…”

Section: Low-rank Tensor Factorizationmentioning

confidence: 99%

A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

Ma¹,

Wang²,

Qin³

2020

Symmetry

View full text Add to dashboard Cite

Low-rank tensor factorization can not only mine the implicit relationships between data but also fill in the missing data when working with complex data. Compared with the traditional collaborative filtering (CF) algorithm, the changes are essentially proposed, from traditional matrix analysis to three-dimensional spatial analysis. Based on low-rank tensor factorization, this paper proposes a recommendation model that comprehensively considers local information and global information, in other words, combining the similarity between trust users and low-rank tensor factorization. First, the similarity between trusted users is measured to capture local information between users by trusting similar preferences of users when selecting items. Then, the users’ similarity is integrated into the tensor, and the low-rank tensor factorization is used to better maintain and describe the internal structure of the data to obtain global information. Furthermore, based on the idea of the alternating least squares method, the conjugate gradient (CG) optimization algorithm for the model of this paper is designed. The local and global information is used to generate the optimal expected result in an iterative process. Finally, we conducted a large number of comparative experiments on the Ciao dataset and the FilmTrust dataset. Experimental results show that the algorithm has less precision loss under the data set with lower density. Thus, not only can a perfect compromise between accuracy and coverage be achieved, but also the computational complexity can be reduced to meet the need for real-time results.

show abstract

Section: Low-rank Tensor Factorizationmentioning

confidence: 99%

A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

Ma¹,

Wang²,

Qin³

2020

Symmetry

View full text Add to dashboard Cite

show abstract

“…Central places in tensor algebra have Tucker and Kruskal tensor forms [38], which allow alternative tensor representations appropriate for certain linear algebraic operations such as tensor-matrix multiplication, tensor compression [39], tensor regularization and factor discovery. Models for tensor data mining have been outlaid in [40]. A very recent work combining tensors and semantics for medical information retrieval is [41].…”

Section: Previous Workmentioning

confidence: 99%

Tensor-Based Semantically-Aware Topic Clustering of Biomedical Documents

et al. 2017

View full text Add to dashboard Cite

Abstract:Biomedicine is a pillar of the collective, scientific effort of human self-discovery, as well as a major source of humanistic data codified primarily in biomedical documents. Despite their rigid structure, maintaining and updating a considerably-sized collection of such documents is a task of overwhelming complexity mandating efficient information retrieval for the purpose of the integration of clustering schemes. The latter should work natively with inherently multidimensional data and higher order interdependencies. Additionally, past experience indicates that clustering should be semantically enhanced. Tensor algebra is the key to extending the current term-document model to more dimensions. In this article, an alternative keyword-term-document strategy, based on scientometric observations that keywords typically possess more expressive power than ordinary text terms, whose algorithmic cornerstones are third order tensors and MeSH ontological functions, is proposed. This strategy has been compared against a baseline using two different biomedical datasets, the TREC (Text REtrieval Conference) genomics benchmark and a large custom set of cognitive science articles from PubMed.

show abstract

“…Another related subject is tensor factorisation, which is of high importance in Data Mining [71] and Machine Learning [15] due to its ability to reduce data dimensionality, find the so-called hidden factors, and even perform information fusion. The closest approaches to ones in the presented study can be found in works on Boolean matrix [7,6] and tensor factorisation [62,5].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Clustering for Community Detection

Ignatov

Semenov

Komissarova

et al. 2017

Formal Concept Analysis of Social Networks

View full text Add to dashboard Cite

Multimodal clustering is an unsupervised technique for mining interesting patterns in n-adic binary relations or n-mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed nsets for n-mode case, etc. Object-attribute biclustering (OA-biclustering) for mining large binary datatables (formal contexts or 2-mode networks) arose by the end of the last decade due to intractability of computation problems related to formal concepts; this type of patterns was proposed as a meaningful and scalable approximation of formal concepts. In this paper, our aim is to present recent advance in OAbiclustering and its extensions to mining multi-mode communities in SNA setting. We also discuss connection between clustering coefficients known in SNA community for 1-mode and 2-mode networks and OA-bicluster density, the main quality measure of an OA-bicluster. Our experiments with 2-, 3-, and 4-mode large realworld networks show that this type of patterns is suitable for community detection in multi-mode cases within reasonable time even though the number of corresponding n-cliques is still unknown due to computation difficulties. An interpretation of OA-biclusters for 1-mode networks is provided as well.

show abstract

Tensors for Data Mining and Data Fusion

Cited by 250 publications

References 133 publications

A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

Tensor-Based Semantically-Aware Topic Clustering of Biomedical Documents

Multimodal Clustering for Community Detection

Contact Info

Product

Resources

About