Semi-Supervised Affinity Propagation with Instance-Level Constraints
نویسندگان
چکیده
Recently, affinity propagation (AP) was introduced as an unsupervised learning algorithm for exemplar based clustering. Here we extend the AP model to account for semisupervised clustering. AP, which is formulated as inference in a factor-graph, can be naturally extended to account for ‘instancelevel’ constraints: pairs of data points that cannot belong to the same cluster (cannotlink), or must belong to the same cluster (must-link). We present a semi-supervised AP algorithm (SSAP) that can use instancelevel constraints to guide the clustering. We demonstrate the applicability of SSAP to interactive image segmentation by using SSAP to cluster superpixels while taking into account user instructions regarding which superpixels belong to the same object. We demonstrate SSAP can achieve better performance compared to other semi-supervised methods.
منابع مشابه
Semi-supervised Affinity Propagation Based on Density Peaks
Original scientific paper In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, a semi-supervised affinity propagation clustering algorithm based on density peaks (SAP-DP) was proposed in this paper. The algorithm uses a new algorithm of density peaks (DP) which has the advantage of the manifold cluster...
متن کاملOn the Comparison of Semi-Supervised Hierarchical Clustering Algorithms in Text Mining Tasks
Semi-supervised clustering approaches have emerged as an option for enhancing clustering results. These algorithms use external information to guide the clustering process. In particular, semi-supervised hierarchical clustering approaches have been explored in many fields in the last years. These algorithms provide efficient and personalized hierarchical overviews of datasets. To the best of th...
متن کاملAn Effective Semi-Supervised Clustering Framework Integrating Pairwise Constraints and Attribute Preferences
Both the instance level knowledge and the attribute level knowledge can improve clustering quality, but how to effectively utilize both of them is an essential problem to solve. This paper proposes a wrapper framework for semi-supervised clustering, which aims to gracely integrate both kinds of priori knowledge in the 598 J. L. Wang, S.Y. Wu, C. Wen, G. Li clustering process, the instance level...
متن کاملInstance-Level Label Propagation with Multi-Instance Learning
Label propagation is a popular semi-supervised learning technique that transfers information from labeled examples to unlabeled examples through a graph. Most label propagation methods construct a graph based on example-to-example similarity, assuming that the resulting graph connects examples that share similar labels. Unfortunately, examplelevel similarity is sometimes badly defined. For inst...
متن کاملAdaptive Semi-supervised Affinity Propagation Clustering Algorithm Based on Structural Similarity
Original scientific paper In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, an adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity (SAAP-SS) is proposed in this paper. First, a novel structural similarity is proposed by solving a non-linear, low-rank rep...
متن کامل