Name
Title | ||
|---|---|---|
Maximum margin multiple instance clustering with applications to image and text clustering. |
Abstract | ||
|---|---|---|
In multiple instance learning problems, patterns are often given as bags and each bag consists of some instances. Most of existing research in the area focuses on multiple instance classification and multiple instance regression, while very limited work has been conducted for multiple instance clustering (MIC). This paper formulates a novel framework, maximum margin multiple instance clustering (M(3)IC), for MIC. However, it is impractical to directly solve the optimization problem of M(3)IC. Therefore, M(3)IC is relaxed in this paper to enable an efficient optimization solution with a combination of the constrained concave-convex procedure and the cutting plane method. Furthermore, this paper presents some important properties of the proposed method and discusses the relationship between the proposed method and some other related ones. An extensive set of empirical results are shown to demonstrate the advantages of the proposed method against existing research for both effectiveness and efficiency. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TNN.2011.2109011 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
image clustering,pattern clustering,multiple instance regression,constrained concave-convex procedure,maximum margin multiple instance clustering,learning (artificial intelligence),cutting plane,multiple instance classification,multiple instance clustering,maximum margin,convex programming,concave-convex procedure,empirical result,optimization problem,multiple instance,concave programming,multiple instance learning,cutting plane method,text analysis,plane method,text clustering,efficient optimization solution,integrated circuit,artificial intelligence,algorithms,convex function,convex functions,optimization,classification,labeling,support vector machine,software design,computer simulation,learning artificial intelligence,clustering,bismuth,support vector machines | Cutting-plane method,Pattern recognition,Computer science,Document clustering,Support vector machine,Supervised learning,Artificial intelligence,Cluster analysis,Artificial neural network,Convex optimization,Optimization problem,Machine learning | Journal |
Volume | Issue | ISSN |
22 | 5 | 1941-0093 |
Citations | PageRank | References |
14 | 0.59 | 30 |
Authors | ||
4 | ||