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35
An experimental investigation of graph kernels on a collaborative recommendation task
 Proceedings of the 6th International Conference on Data Mining (ICDM 2006
, 2006
"... This paper presents a survey as well as a systematic empirical comparison of seven graph kernels and two related similarity matrices (simply referred to as graph kernels), namely the exponential diffusion kernel, the Laplacian exponential diffusion kernel, the von Neumann diffusion kernel, the regul ..."
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Cited by 27 (7 self)
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This paper presents a survey as well as a systematic empirical comparison of seven graph kernels and two related similarity matrices (simply referred to as graph kernels), namely the exponential diffusion kernel, the Laplacian exponential diffusion kernel, the von Neumann diffusion kernel, the regularized Laplacian kernel, the commutetime kernel, the randomwalkwithrestart similarity matrix, and finally, three graph kernels introduced in this paper: the regularized commutetime kernel, the Markov diffusion kernel, and the crossentropy diffusion matrix. The kernelonagraph approach is simple and intuitive. It is illustrated by applying the nine graph kernels to a collaborativerecommendation task and to a semisupervised classification task, both on several databases. The graph methods compute proximity measures between nodes that help study the structure of the graph. Our comparisons suggest that the regularized commutetime and the Markov diffusion kernels perform best, closely followed by the regularized Laplacian kernel. 1
Clustering graphs by weighted substructure mining. ICML
 Proc. ICML 2006, 953– 960
, 2006
"... Graph data is getting increasingly popular in, e.g., bioinformatics and text processing. A main difficulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary feature vector of indicators of all possible subgraphs, the dimensi ..."
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Cited by 19 (3 self)
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Graph data is getting increasingly popular in, e.g., bioinformatics and text processing. A main difficulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary feature vector of indicators of all possible subgraphs, the dimensionality gets too large for usual statistical methods. We propose an efficient method for learning a binomial mixture model in this feature space. Combining the ℓ1 regularizer and the data structure called DFS code tree, the MAP estimate of nonzero parameters are computed efficiently by means of the EM algorithm. Our method is applied to the clustering of RNA graphs, and is compared favorably with graph kernels and the spectral graph distance. 1.
Transforming strings to vector spaces using prototype selection
, 2006
"... Abstract. A common way of expressing string similarity in structural pattern recognition is the edit distance. It allows one to apply the kNN rule in order to classify a set of strings. However, compared to the wide range of elaborated classifiers known from statistical pattern recognition, this is ..."
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Cited by 14 (5 self)
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Abstract. A common way of expressing string similarity in structural pattern recognition is the edit distance. It allows one to apply the kNN rule in order to classify a set of strings. However, compared to the wide range of elaborated classifiers known from statistical pattern recognition, this is only a very basic method. In the present paper we propose a method for transforming strings into ndimensional real vector spaces based on prototype selection. This allows us to subsequently classify the transformed strings with more sophisticated classifiers, such as support vector machine and other kernel based methods. In a number of experiments, we show that the recognition rate can be significantly improved by means of this procedure. 1
RegionBased Hierarchical Image Matching
 INT J COMPUT VIS
, 2007
"... This paper presents an approach to regionbased hierarchical image matching, where, given two images, the goal is to identify the largest part in image 1 and its match in image 2 having the maximum similarity measure defined in terms of geometric and photometric properties of regions (e.g., area, b ..."
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Cited by 14 (6 self)
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This paper presents an approach to regionbased hierarchical image matching, where, given two images, the goal is to identify the largest part in image 1 and its match in image 2 having the maximum similarity measure defined in terms of geometric and photometric properties of regions (e.g., area, boundary shape, and color), as well as region topology (e.g., recursive embedding of regions). To this end, each image is represented by a tree of recursively embedded regions, obtained by a multiscale segmentation algorithm. This allows us to pose image matching as the tree matching problem. To overcome imaging noise, onetoone, manytoone, and manytomany node correspondences are allowed. The trees are first augmented with new nodes generated by merging adjacent sibling nodes, which produces directed acyclic graphs (DAGs). Then, transitive closures of the DAGs are constructed, and the tree matching problem reformulated as finding a bijection between the two transitive closures on DAGs, while preserving the connectivity and ancestordescendant relationships of the original trees. The proposed approach is validated on real images showing similar objects, captured under different types of noise, including differences in lighting conditions, scales, or viewpoints, amidst limited occlusion and clutter.
Recommendation on item graphs
 In ICDM’06
, 2006
"... A novel scheme for itembased recommendation is proposed in this paper. In our framework, the items are described by an undirected weighted graph G = (V, E). V is the node set which is identical to the item set, and E is the edge set. Associate with each edge eij ∈ E is a weight wij 0, which represe ..."
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Cited by 14 (3 self)
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A novel scheme for itembased recommendation is proposed in this paper. In our framework, the items are described by an undirected weighted graph G = (V, E). V is the node set which is identical to the item set, and E is the edge set. Associate with each edge eij ∈ E is a weight wij 0, which represents similarity between items i and j. Without the loss of generality, we assume that any user’s ratings to the items should be sufficiently smooth with respect to the intrinsic structure of the items, i.e., a user should give similar ratings to similar items. A simple algorithm is presented to achieve such a “smooth ” solution. Encouraging experimental results are provided to show the effectiveness of our method. 1.
An experimental investigation of kernels on graphs for collaborative . . .
 NEURAL NETWORKS
, 2012
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Vector space embedding of undirected graphs with fixedcardinality vertex sequences for classification
 in Proc. 20th Int. Conf. Pattern Recognition (ICPR
, 2010
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Exact and inexact graph matching: methodology and applications
 Managing and Mining Graph Data, volume 40 of Advances in Database Systems
, 2010
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On Euclidean Corrections for NonEuclidean Dissimilarities
"... Abstract. NonEuclidean dissimilarity measures can be well suited for building representation spaces that are more beneficial for pattern classification systems than the related Euclidean ones [1,2]. A nonEuclidean representation space is however cumbersome for training classifiers, as many statis ..."
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Cited by 7 (5 self)
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Abstract. NonEuclidean dissimilarity measures can be well suited for building representation spaces that are more beneficial for pattern classification systems than the related Euclidean ones [1,2]. A nonEuclidean representation space is however cumbersome for training classifiers, as many statistical techniques rely on the Euclidean inner product that is missing there. In this paper we report our findings on the applicability of corrections that transform a nonEuclidean representation space into a Euclidean one in which similar or better classifiers can be trained. In a casestudy based on four principally different classifiers we find out that standard correction procedures fail to construct an appropriate Euclidean space, equivalent to the original nonEuclidean one. 1
An inexact graph comparison approach in joint eigenspace
 In SSPR/SPR
, 2008
"... Abstract. In graph comparison, the use of (dis)similarity measurements between graphs is an important topic. In this work, we propose an eigendecomposition based approach for measuring dissimilarities between graphs in the joint eigenspace (JoEig). We will compare our JoEig approach with two other ..."
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Cited by 7 (4 self)
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Abstract. In graph comparison, the use of (dis)similarity measurements between graphs is an important topic. In this work, we propose an eigendecomposition based approach for measuring dissimilarities between graphs in the joint eigenspace (JoEig). We will compare our JoEig approach with two other eigendecomposition based methods that compare graphs in different eigenspaces. To calculate the dissimilarity between graphs of different sizes and perform inexact graph comparison, we further develop three different ways to resize the eigenspectra and study their performance in different situations. 1