Non-negative mixtures

This is the author's accepted pre-print of the article, first published as M. D. Plumbley, A. Cichocki and R. Bro. Non-negative mixtures. In P. Comon and C. Jutten (Ed), Handbook of Blind Source Separation: Independent Component Analysis and Applications. Chapter 13, pp. 515-547. Academic Press, Feb 2010. ISBN 978-0-12-374726-6 DOI: 10.1016/B978-0-12-374726-6.00018-7file: Proof:p\PlumbleyCichockiBro10-non-negative.pdf:PDF owner: markp timestamp: 2011.04.26file: Proof:p\PlumbleyCichockiBro10-non-negative.pdf:PDF owner: markp timestamp: 2011.04.2

Clustering Boolean Tensors

Tensor factorizations are computationally hard problems, and in particular, are often significantly harder than their matrix counterparts. In case of Boolean tensor factorizations -- where the input tensor and all the factors are required to be binary and we use Boolean algebra -- much of that hardness comes from the possibility of overlapping components. Yet, in many applications we are perfectly happy to partition at least one of the modes. In this paper we investigate what consequences does this partitioning have on the computational complexity of the Boolean tensor factorizations and present a new algorithm for the resulting clustering problem. This algorithm can alternatively be seen as a particularly regularized clustering algorithm that can handle extremely high-dimensional observations. We analyse our algorithms with the goal of maximizing the similarity and argue that this is more meaningful than minimizing the dissimilarity. As a by-product we obtain a PTAS and an efficient 0.828-approximation algorithm for rank-1 binary factorizations. Our algorithm for Boolean tensor clustering achieves high scalability, high similarity, and good generalization to unseen data with both synthetic and real-world data sets

Image Tagging using Modified Association Rule based on Semantic Neighbors

With the rapid development of the internet, mobiles, and social image-sharing websites, a large number of images are generated daily.  The huge repository of the images poses challenges for an image retrieval system. On image-sharing social websites such as Flickr, the users can assign keywords/tags to the images which can describe the content of the images. These tags play important role in an image retrieval system. However, the user-assigned tags are highly personalized which brings many challenges for retrieval of the images.  Thus, it is necessary to suggest appropriate tags to the images. Existing methods for tag recommendation based on nearest neighbors ignore the relationship between tags. In this paper, the method is proposed for tag recommendations for the images based on semantic neighbors using modified association rule. Given an image, the method identifies the semantic neighbors using random forest based on the weight assigned to each category. The tags associated with the semantic neighbors are used as candidate tags. The candidate tags are expanded by mining tags using modified association rules where each semantic neighbor is considered a transaction. In modified association rules, the probability of each tag is calculated using TF-IDF and confidence value. The experimentation is done on Flickr, NUS-WIDE, and Corel-5k datasets. The result obtained using the proposed method gives better performance as compared to the existing tag recommendation methods