On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Parallel multilevel k-way partitioning scheme for irregular graphs
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Audio-visual synchrony for detection of monologues in video archives
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Semi-Supervised Kernel Regression
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A survey of kernel and spectral methods for clustering
Pattern Recognition
A tutorial on spectral clustering
Statistics and Computing
Probabilistic latent semantic visualization: topic model for visualizing documents
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Semi-supervised kernel density estimation for video annotation
Computer Vision and Image Understanding
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
Semi-Supervised Learning
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
Parallel Spectral Clustering in Distributed Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
C3E: a framework for combining ensembles of classifiers and clusterers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Voting based extreme learning machine
Information Sciences: an International Journal
A unified framework for web video topic discovery and visualization
Pattern Recognition Letters
A Link-Based Cluster Ensemble Approach for Categorical Data Clustering
IEEE Transactions on Knowledge and Data Engineering
Supervised subspace projections for constructing ensembles of classifiers
Information Sciences: an International Journal
Combining supervised and unsupervised models via unconstrained probabilistic embedding
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Fusion of supervised and unsupervised learning for improved classification of hyperspectral images
Information Sciences: an International Journal
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In this study, we consider an ensemble problem in which we combine outputs coming from models developed in the supervised and unsupervised modes. By jointly considering the grouping results coming from unsupervised models we aim to improve the classification accuracy of supervised model ensemble. Here, we formulate the ensemble task as an Unconstrained Probabilistic Embedding (UPE) problem. Specifically, we assume both objects and classes/clusters have latent coordinates without constraints in a D-dimensional Euclidean space, and consider the mapping from the embedded space into the space of model results as a probabilistic generative process. A solution to this embedding can be obtained using the quasi-Newton method, which makes objects and classes/clusters with high co-occurrence weights are embedded close. Then, prediction is determined by taking the distances between the object and the classes in the embedded space. We demonstrate the benefits of this unconstrained embedding method by running extensive and systematic experiments on real-world datasets. Furthermore, we conduct experiments to investigate how the quality and the number of clustering models affect the performance of this ensemble method. We also show the robustness of the proposed model.