SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities

Pernes, Diogo; Cardoso, Jaime S.

doi:10.1109/ijcnn.2019.8851929

Cited by 4 publications

(3 citation statements)

References 23 publications

(35 reference statements)

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“…For details on the rest of the update equations of the parameters during EM, we refer to (Pernes and Cardoso 2019).…”

Section: Mixtures Of Hidden Markov Modelsmentioning

confidence: 99%

“…Various approaches have been proposed to improve upon using Expectation Maximization (EM), as introduced by (Dempster, Laird, and Rubin 1977), which is the standard approach of inferring MHMMs (Bishop 2006). For example, in (Subakan, Traa, and Smaragdis 2014) and (Pernes and Cardoso 2019), the improvements are w.r.t. time complexity and sparsity of the model, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Towards interpretability of Mixtures of Hidden Markov Models

Safinianaini,

Boström

2021

Preprint

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Mixtures of Hidden Markov Models (MHMMs) are frequently used for clustering of sequential data. An important aspect of MHMMs, as of any clustering approach, is that they can be interpretable, allowing for novel insights to be gained from the data. However, without a proper way of measuring interpretability, the evaluation of novel contributions is difficult and it becomes practically impossible to devise techniques that directly optimize this property. In this work, an information-theoretic measure (entropy) is proposed for interpretability of MHMMs, and based on that, a novel approach to improve model interpretability is proposed, i.e., an entropy-regularized Expectation Maximization (EM) algorithm. The new approach aims for reducing the entropy of the Markov chains (involving state transition matrices) within an MHMM, i.e., assigning higher weights to common state transitions during clustering. It is argued that this entropy reduction, in general, leads to improved interpretability since the most influential and important state transitions of the clusters can be more easily identified. An empirical investigation shows that it is possible to improve the interpretability of MHMMs, as measured by entropy, without sacrificing (but rather improving) clustering performance and computational costs, as measured by the v-measure and number of EM iterations, respectively.

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“…For details on the rest of the update equations of the parameters during EM, we refer to (Pernes and Cardoso 2019).…”

Section: Mixtures Of Hidden Markov Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards interpretability of Mixtures of Hidden Markov Models

Safinianaini,

Boström

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Attributed networks [1] are ubiquitous in the real world such as social networks [2], communication networks [3], and product co-purchase networks [4], in which each node is associated with a rich set of attributes or characteristics, in addition to the raw network topology.…”

Section: Introductionmentioning

confidence: 99%

Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks

Fan

Zhang

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure and node attribute. In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. Specifically, Anoma-lyDAE consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. Moreover, attention mechanism is employed in structure encoder to learn the importance between a node and its neighbors for an effective capturing of structure pattern, which is important to anomaly detection. Besides, by taking both the node embedding and attribute embedding as inputs of attribute decoder, the cross-modality interactions between network structure and node attribute are learned during the reconstruction of node attribute. Finally, anomalies can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.

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