Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Multivariate Information Bottleneck
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Sufficient dimensionality reduction
The Journal of Machine Learning Research
Sequential information bottleneck for finite data
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning Hidden Variable Networks: The Information Bottleneck Approach
The Journal of Machine Learning Research
Information Bottleneck for Gaussian Variables
The Journal of Machine Learning Research
Visual cue cluster construction via information bottleneck principle and kernel density estimation
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Unsupervised image-set clustering using an information theoretic framework
IEEE Transactions on Image Processing
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Recent years have witnessed a growing interest in the information bottleneck theory. Among the relevant algorithms in the extant literature, the sequential Information Bottleneck (sIB) algorithm is recognized for its balance between accuracy and complexity. However, like many other optimization techniques, it still suffers from the problem of getting easily trapped in local optima. To that end, our study proposed an iterative sIB algorithm (isIB) based on mutation for the clustering problem. From initial solution vectors of cluster labels generated by a seeding the sIB algorithm, our algorithm randomly selects a subset of elements and mutates the cluster labels according to the optimal mutation rate. The results are iteratively optimized further using genetic algorithms. Finally, the experimental results on the benchmark data sets validate the advantage of our iterative sIB algorithm over the sIB algorithm in terms of both accuracy and efficiency.