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Music recommendation by unified hypergraph: combining social media information and music content
- In Proceedings of the 18th annual ACM international conference on Multimedia
, 2010
"... Acoustic-based music recommender systems have received increasing interest in recent years. Due to the semantic gap between low level acoustic features and high level music concepts, many researchers have explored collaborative filtering techniques in music recommender systems. Traditional collabora ..."
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Acoustic-based music recommender systems have received increasing interest in recent years. Due to the semantic gap between low level acoustic features and high level music concepts, many researchers have explored collaborative filtering techniques in music recommender systems. Traditional collaborative filtering music recommendation methods only focus on user rating information. However, there are various kinds of social media information, including different types of objects and relations among these objects, in music social communities, such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. In this paper, we propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model highorder relations. Experiments on a data set collected from the music social community Last.fm have demonstrated the effectiveness of our proposed algorithm.
Robust clustering as ensemble of affinity relations
- In Neural Info. Proc. Systems (NIPS
, 2010
"... In this paper, we regard clustering as ensembles of k-ary affinity relations and clusters correspond to subsets of objects with maximal average affinity relations. The average affinity relation of a cluster is relaxed and well approximated by a constrained homogenous function. We present an efficien ..."
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Cited by 15 (4 self)
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In this paper, we regard clustering as ensembles of k-ary affinity relations and clusters correspond to subsets of objects with maximal average affinity relations. The average affinity relation of a cluster is relaxed and well approximated by a constrained homogenous function. We present an efficient procedure to solve this optimization problem, and show that the underlying clusters can be robustly revealed by using priors systematically constructed from the data. Our method can automatically select some points to form clusters, leaving other points un-grouped; thus it is inherently robust to large numbers of outliers, which has seriously limited the applicability of classical methods. Our method also provides a unified solution to clustering from k-ary affinity relations with k ≥ 2, that is, it applies to both graph-based and hypergraph-based clustering problems. Both theoretical analysis and experimental results show the superiority of our method over classical solutions to the clustering problem, especially when there exists a large number of outliers. 1
What is a Cluster? Perspectives from Game Theory
"... “Since no paradigm ever solves all the problems it defines and since no two paradigms leave all the same problems unsolved, paradigm debates always involve the question: Which problems is it more significant to have solved?” Thomas S. Kuhn, The Structure of Scientific Revolutions (1962) There is no ..."
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Cited by 12 (0 self)
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“Since no paradigm ever solves all the problems it defines and since no two paradigms leave all the same problems unsolved, paradigm debates always involve the question: Which problems is it more significant to have solved?” Thomas S. Kuhn, The Structure of Scientific Revolutions (1962) There is no shortage of clustering algorithms, and recently a new wave of excitement has spread across the machine learning community mainly because of the important development of spectral methods. At the same time, there is also growing interest around fundamental questions pertaining to the very nature of the clustering problem (see, e.g., [17, 1, 28]). Yet, despite the tremendous progress in the field, the clustering problem remains elusive and a satisfactory answer even to the most basic questions is still to come. Upon scrutinizing the relevant literature on the subject, it becomes apparent that the vast majority of the existing approaches deal with a very specific version of the problem, which asks for partitioning the input data into coherent classes. In fact, almost invariably, the problem of clustering is defined as a partitioning problem, and even the classical distinction between hierarchical and partitional algorithms
Graph Transduction as a Noncooperative Game
, 2012
"... Graph transduction is a popular class of semisupervised learning techniques that aims to estimate a classification function defined over a graph of labeled and unlabeled data points. The general idea is to propagate the provided label information to unlabeled nodes in a consistent way. In contrast t ..."
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Cited by 7 (2 self)
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Graph transduction is a popular class of semisupervised learning techniques that aims to estimate a classification function defined over a graph of labeled and unlabeled data points. The general idea is to propagate the provided label information to unlabeled nodes in a consistent way. In contrast to the traditional view, in which the process of label propagation is defined as a graph Laplacian regularization, this article proposes a radically different perspective, based on game-theoretic notions. Within the proposed framework, the transduction problem is formulated in terms of a noncooperative multiplayer game whereby equilibria correspond to consistent labelings of the data. An attractive feature of this formulation is that it is inherently a multiclass approach and imposes no constraint whatsoever on the structure of the pairwise similarity matrix, being able to naturally deal with asymmetric and negative similarities alike. Experiments on a number of real-world problems demonstrate that the proposed approach performs well compared with state-of-the-art algorithms, and it can deal effectively with various types of similarity relations.
Efficient Hypergraph Clustering
"... Data clustering is an essential problem in data mining, machine learning and computer vision. In this paper we present a novel method for the hypergraph clustering problem, in which second or higher order affinities between sets of data points are considered. Our algorithm has important theoretical ..."
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Cited by 4 (1 self)
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Data clustering is an essential problem in data mining, machine learning and computer vision. In this paper we present a novel method for the hypergraph clustering problem, in which second or higher order affinities between sets of data points are considered. Our algorithm has important theoretical properties, such as convergence and satisfaction of first order necessary optimality conditions. It is based on an efficient iterative procedure, which by updating the cluster membership of all points in parallel, is able to achieve state of the art results in very few steps. We outperform current hypergraph clustering methods especially in terms of computational speed, but also in terms of accuracy. Moreover, we show that our method could be successfully applied both to higher-order assignment problems and to image segmentation. 1
Using Rich Social Media Information for Music Recommendation via Hypergraph Model
"... There are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two main challenges to exploit this rich soc ..."
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Cited by 4 (2 self)
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There are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two main challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. We propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations.
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model
"... Spectral graph partitioning methods have received significant attention from both practitioners and theorists in computer science. Some notable studies have been carried out regarding the behavior of these methods for infinitely large sample size (von Luxburg et al., 2008; Rohe et al., 2011), which ..."
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Cited by 2 (2 self)
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Spectral graph partitioning methods have received significant attention from both practitioners and theorists in computer science. Some notable studies have been carried out regarding the behavior of these methods for infinitely large sample size (von Luxburg et al., 2008; Rohe et al., 2011), which provide sufficient confidence to practitioners about the effectiveness of these methods. On the other hand, recent developments in computer vision have led to a plethora of applications, where the model deals with multi-way affinity relations and can be posed as uniform hyper-graphs. In this paper, we view these models as random m-uniform hypergraphs and establish the consistency of spectral algorithm in this general setting. We de-velop a planted partition model or stochastic blockmodel for such problems using higher order tensors, present a spectral technique suited for the purpose and study its large sample behavior. The analysis reveals that the algorithm is consistent for m-uniform hypergraphs for larger values of m, and also the rate of convergence improves for increasing m. Our result provides the first theoretical evidence that establishes the importance of m-way affinities. 1
Designing Labeled Graph Classifiers by Exploiting the Rényi Entropy of the Dissimilarity Representation
, 2014
"... Representing patterns by complex relational structures, such as labeled graphs, is becoming an increas-ingly common practice in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowa ..."
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Representing patterns by complex relational structures, such as labeled graphs, is becoming an increas-ingly common practice in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various labeled graph data types. However, the design of effective learning and mining procedures operating in the space of labeled graphs is still a challenging problem, especially from the computational complexity viewpoint. In this paper, we present a major improvement of a general-purpose graph classification system, which is conceived on an interplay among dissimilarity representation, clus-tering, information-theoretic techniques, and evolutionary optimization. The improvement focuses on a specific key subroutine of the system that performs the compression of the input data. We prove different theorems which are fundamental to the setting of such a compression operation. We demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well-known datasets of labeled graphs, considering as distinct performance indicators the classification accuracy, the computing time, and the parsimony in terms of structural complexity of the synthesized classification model. Over-all, the results show state-of-the-art standards in terms of test set accuracy, while achieving considerable reductions for what concerns the effective computing time and classification model complexity.
The Total Variation on Hypergraphs- Learning on Hypergraphs Revisited
"... Hypergraphs allow one to encode higher-order relationships in data and are thus a very flexible modeling tool. Current learning methods are either based on approx-imations of the hypergraphs via graphs or on tensor methods which are only appli-cable under special conditions. In this paper, we presen ..."
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Cited by 2 (1 self)
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Hypergraphs allow one to encode higher-order relationships in data and are thus a very flexible modeling tool. Current learning methods are either based on approx-imations of the hypergraphs via graphs or on tensor methods which are only appli-cable under special conditions. In this paper, we present a new learning framework on hypergraphs which fully uses the hypergraph structure. The key element is a family of regularization functionals based on the total variation on hypergraphs. 1
