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Topic identification (TID) is a technique associated with labelling a set of textual documents with a meaningful label representing its content. TID for online news presents different problems from TID for other corpora, such as the large data volume and the frequently updated topic. Moreover, the number of developing methods for Indonesian corpus is rather small. Brace well's algorithm has been proven...
Both image alignment and image clustering are widely researched with numerous applications in recent years. These two problems are traditionally studied separately. However in many real world applications, both alignment and clustering results are needed. Recent study has shown that alignment and clustering are two highly coupled problems. Thus we try to solve the two problems in a unified framework...
Document clustering has become inevitable for applications that aim to extract information from huge corpuses. Such applications face two main challenges; one is the efficient representation of the documents, along with using an efficient similarity measure, and the second is dealing with the dynamic nature of the corpus. In this paper, an efficient document clustering model is introduced for incrementally...
We have investigated a technique for recognising faces invariant of facial expressions. We apply multi-linear tensor algebra, which subsumes linear algebra, to analyse and recognise 3D face surfaces. This potent framework possesses a remarkable ability to deal with the shortcomings of principle component analysis in less constrained situations. A set of vector spaces can be used to represent the variation...
In this paper, an improved Locally Linear Embedding method is proposed and used in the multi-pose ear recognition. In the traditional LLE method, the neighbors of a data point are selected by using k-nearest neighbor algorithm or e-nearest neighbor algorithm. Both of these methods neglect the different neighborhood of each data point and use a uniform way to select neighbors which leads to the sensitiveness...
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