Diffusion Processes for Retrieval Revisited

Donoser, Michael; Bischof, Horst

doi:10.1109/cvpr.2013.174

Cited by 174 publications

(166 citation statements)

References 11 publications

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“…diffusion processes were e.g. proposed in [2,30,31,32] and in a recent paper [6] these methods are summarized in a general diffusion framework.…”

Section: Introductionmentioning

confidence: 99%

Replicator Graph Clustering

Donoser¹

2013

Procedings of the British Machine Vision Conference 2013

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In this paper we introduce an efficient, effective and scalable clustering method denoted as Replicator Graph Clustering. Our method takes measures of similarity between pairs of data points (i. e. an affinity matrix) as input and identifies a set of clusters and unique cluster assignments in a fully unsupervised manner, where the cluster granularity is adaptable by a single parameter. We provide clustering results in three subsequent steps: (a) diffusing affinities by finding personalized evolutionary stable strategies of non-cooperative games (b) building a mutual k-nearest neighbor graph representing the underlying manifold and (c) applying a graph based clustering strategy which identifies the final clusters. Individual steps have low computational complexity which leads to an efficient clustering method, scaling well with an increasing number of data points. Experimental evaluation demonstrates competitive performance to state-of-the-art in several application fields.

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“…diffusion processes were e.g. proposed in [2,30,31,32] and in a recent paper [6] these methods are summarized in a general diffusion framework.…”

Section: Introductionmentioning

confidence: 99%

Replicator Graph Clustering

Donoser¹

2013

Procedings of the British Machine Vision Conference 2013

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“…In the literature, a number of diffusion models have been proposed by tuning the functions for W for different application domains [39,36,38]. Our studies also reveal the choice of diffusion scheme has a substantial impact on the link prediction accuracy.…”

Section: Diffusion Processmentioning

confidence: 67%

“…In the past few years, the diffusion process (DP) model has attracted an increasing amount of interest for solving information retrieval problems in different domains [36][37][38]. DP aims to capture the geometry of the underlying manifold in a weighted graph that represents the proximity of the instances.…”

Section: Unsupervised Diffusion-based Link Prediction Modelsmentioning

confidence: 99%

Link Prediction in Heterogeneous Collaboration Networks

Wang

Sukthankar

2014

Lecture Notes in Social Networks

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Abstract. Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this article, we study both supervised and unsupervised link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new supervised link prediction framework, Link Prediction using Social Features (LPSF ), which incorporates a reweighting scheme for the network based on nodes' features extracted from patterns of prominent interactions across the network.Experiments on coauthorship networks demonstrate that the choice for measuring link weights can be critical for the link prediction task. Our proposed reweighting method in LPSF better expresses the intrinsic relationship between nodes and improves prediction accuracy for supervised link prediction techniques. We also compare the unsupervised performance of the individual features used within LPSF with two new diffusion-based methods: LPDP (Link Prediction using Diffusion Process) and LPDM (Link Prediction using Diffusion Maps). Experiments demonstrate that LPDP is able to identify similar node pairs, even far away ones, that are connected by weak ties in the coauthorship network using the diffusion process; however, reweighting the network has little impact on prediction performance.

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“…A review of these techniques, in the context of image retrieval, is presented in [12]. Most of these methods have the form above, using a different transition matrix P. Much less attention has been devoted to the determination of the seed vector s. This is not surprising, since in retrieval applications this is simply a binary vector that indicates query images.…”

Section: Saliency Seedsmentioning

confidence: 99%