Non‐negative residual matrix factorization: problem definition, fast solutions, and applications

Tong, Hanghang; Lin, Ching‐Yung

doi:10.1002/sam.10148

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Yang and Leskovec [40] detects overlapping communities by applying NMF to very large social networks. Even though much of the literature on NMF for social networks pertains to clustering and community detection, Tong and Lin [36] examines a related problem of anomaly detection in social networks using a nonnegative matrix factorization with a nonnegativity constrained additive residual representing the anomalous edges. The additive component is sparse and nonnegative thus containing edges which deviate from the low rank structure of the adjacency matrix.…”

Section: Relevant Literaturementioning

confidence: 99%

Behavioral clusters in dynamic graphs

et al. 2015

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a b s t r a c tThis paper contributes a method for combining sparse parallel graph algorithms with dense parallel linear algebra algorithms in order to understand dynamic graphs including the temporal behavior of vertices. Our method is the first to cluster vertices in a dynamic graph based on arbitrary temporal behaviors. In order to successfully implement this method, we develop a feature based pipeline for dynamic graphs and apply Nonnegative Matrix Factorization (NMF) to these features. We demonstrate these steps with a sample of the Twitter mentions graph as well as a CAIDA network traffic graph. We contribute and analyze a parallel NMF algorithm presenting both theoretical and empirical studies of performance. This work can be leveraged by graph/network analysts to understand the temporal behavior cluster structure and segmentation structure of dynamic graphs.Ó 2015 Published by Elsevier B.V.

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Section: Relevant Literaturementioning

confidence: 99%

Behavioral clusters in dynamic graphs

et al. 2015

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“…Although similar, these two tasks have very different objectives. Outlier detection [9,21,24,27] aims at finding an object that is different from objects in a cluster, whereas outlying aspect detection [3,12,23,29,35] is to find the most interesting attribute (i.e. the outlying aspect) of a given object compared with a set of peer objects.…”

Section: Related Workmentioning

confidence: 99%

Context-aware Outstanding Fact Mining from Knowledge Graphs

Yang

Karras

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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An Outstanding Fact (OF) is an attribute that makes a target entity stand out from its peers. The mining of OFs has important applications, especially in Computational Journalism, such as news promotion, fact-checking, and news story finding. However, existing approaches to OF mining: (i) disregard the context in which the target entity appears, hence may report facts irrelevant to that context; and (ii) require relational data, which are often unavailable or incomplete in many application domains.In this paper, we introduce the novel problem of mining Contextaware Outstanding Facts (COFs) for a target entity under a given context specified by a context entity. We propose FMiner, a contextaware mining framework that leverages knowledge graphs (KGs) for COF mining. FMiner generates COFs in two steps. First, it discovers top-relevant relationships between the target and the context entity from a KG. We propose novel optimizations and pruning techniques to expedite this operation, as this process is very expensive on large KGs due to its exponential complexity. Second, for each derived relationship, we find the attributes of the target entity that distinguish it from peer entities that have the same relationship with the context entity, yielding the top-COFs. As such, the mining process is modeled as a top-( , ) search problem. Context-awareness is ensured by relying on the relevant relationships with the context entity to derive peer entities for COF extraction. Consequently, FMiner can effectively navigate the search to obtain context-aware OFs by incorporating a context entity. We conduct extensive experiments, including a user study, to validate the efficiency and the effectiveness of FMiner. CCS CONCEPTS• Information systems → Data mining; Collaborative and social computing systems and tools.

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“…Over the past few years, the nonnegative matrix factorization algorithm (NMF) [ 1 ] and its variants have proven to be useful for several problems, especially in facial image characterization and representation problems [ 2 – 8 ]. The idea of nonnegative factorization is partly motivated by the biological fact that the firing rates in visual perception neurons are nonnegative.…”

Section: Introductionmentioning

confidence: 99%

Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition

Zhang

Chang²

2014

The Scientific World Journal

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In order to overcome the limitation of traditional nonnegative factorization algorithms, the paper presents a generalized discriminant orthogonal non-negative tensor factorization algorithm. At first, the algorithm takes the orthogonal constraint into account to ensure the nonnegativity of the low-dimensional features. Furthermore, the discriminant constraint is imposed on low-dimensional weights to strengthen the discriminant capability of the low-dimensional features. The experiments on facial expression recognition have demonstrated that the algorithm is superior to other non-negative factorization algorithms.

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Non‐negative residual matrix factorization: problem definition, fast solutions, and applications

Cited by 5 publications

References 43 publications

Behavioral clusters in dynamic graphs

Behavioral clusters in dynamic graphs

Context-aware Outstanding Fact Mining from Knowledge Graphs

Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition

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