Data involving repeated measurements of several variables over different factors, experimental conditions or time may exhibit correlations among variables, as well as between factors. The discovery of these underlying, meaningful relations is important to a wide variety of areas such as psychology, signal processing, finance, among others. Common methods such as independent component analysis, ...

Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. We interpret the factorization in a new way and use it to generate missing attributes from test data. We provide a joint optimization scheme for the missing attributes as well as the NMF factors. We prove the monotonic convergence of our algorithms. We present classification res...

We propose a novel approach to annotating weakly labelled data. In contrast to many existing approaches that perform annotation by seeking clusters of self-similar exemplars (minimising intra-class variance), we perform image annotation by selecting exemplars that have never occurred before in the much larger, and strongly annotated, negative training set (maximising inter-class variance). Comp...

Over the years, data mining has attracted most of the attention from the research community. The researchers attempt to develop faster, more scalable algorithms to navigate over the ever increasing volumes of spatial gene expression data in search of meaningful patterns. Association rules are a data mining technique that tries to identify intrinsic patterns in spatial gene expression data. It h...

High dimensional data suffer from unwanted variation, such as the batch effects common in microarray data. Unwanted variation complicates the analysis of high dimensional data, leading to high rates of false discoveries, high rates of missed discoveries, or both. In many cases the factors causing the unwanted variation are unknown and must be inferred from the data. In such cases, negative cont...

We present a non-negative inductive latent factor model for binaryand count-valued matrices containing dyadic data, with side information along the rows and/or the columns of the matrix. The side information is incorporated by conditioning the row and column latent factors on the available side information via a regression model. Our model can not only perform matrix factorization and completio...

Ever increasing amount of data has led to the fact that the data quality has had a crucial impact on almost any IT applications. Nonetheless, data quality issues are nearly omnipresent. Recently, a great deal of researches has focused on improving data quality. Bad quality of the data can cause incorrect decision making. It is normally infeasible to guarantee sufficient data quality through man...

The properties of training data set such as size, distribution and number of attributes significantly contribute to the generalization error of a learning machine. A not-well-distributed data set is prone to lead to a partial overfitting model. The two approaches proposed in this paper for the binary classification enhance the useful data information by mining negative data. First, error driven...

Liquid Chromatography-Mass Spectrometry (LC/MS) provides large datasets from which one needs to extract the relevant information. Since these data are made of non-negative mixtures of non-negative mass spectra, nonnegative matrix factorization (NMF) is well suited for its processing, but it has barely been used in LC/MS. Also, these data are very difficult to deal with since they are usually co...