Robust Semi-supervised Nonnegative Matrix Factorization

PG research

We would like to invite you to the latest research seminar of the Creative Technology Research Centre.Robust_Semi-supervised_Nonnegative_Matrix_Factorization

 

Speaker: Jing Wang

 

Title:   Robust Semi-supervised Nonnegative Matrix Factorization

 

Time: 2:00PM-3:00PM

Date: Wednesday 2nd December 2015

Room: P302 LT, Poole House, Talbot Campus

 

Abstract: Clustering aims to organize a collection of data items into clusters, such that items within a cluster are more “similar” to each other than to those in the other clusters, which has been used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Clustering is usually performed when no information is available concerning the membership of data items to predefined classes. For this reason, it is traditionally seen as part of unsupervised learning. However, in reality, it is often the case that some data information (e.g. labels) is available and could be used to bias the clustering for producing considerable improvements in learning accuracy. Also, data have some new challenges, such as high- dimensionality, sparsity, containing noises and outliers, etc. This motivates us to develop new technology to deal with this kind of complex data. To address all these issues, we propose semi-supervised nonnegative matrix factorization approaches. Experiments carried on well-known data sets demonstrate the effectiveness.

 

We hope to see you there.

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