Recently, a considerable growth of interest in using Nonnegative Matrix Factorization (NMF) for pattern classification and data clustering has been observed. For nonnegative data (observations, data items, feature vectors) many problems of partitional clustering can be modeled in terms of a matrix factorization into two groups of vectors: the nonnegative centroid vectors and the binary vectors ...

Topic models have been extensively used to organize and interpret the contents of large, unstructured corpora of text documents. Although topic models often perform well on traditional training vs. test set evaluations, it is often the case that the results of a topic model do not align with human interpretation. This interpretability fallacy is largely due to the unsupervised nature of topic m...

Non-negative matrix factorization (NMF) has become a popular technique for finding low-dimensional representations of data. While the standard NMF can only be performed in the original feature space, one variant of NMF, named concept factorization, can be naturally kernelized and inherits all the strengths of NMF. To make use of label information, we propose a semi-supervised concept this paper...

Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost fun...

Nonnegative Matrix Factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be partsbased in the human brain. However, when labeled and unlabeled images are sampled from different distributions, they may be quantized into different basis vector space and represented in different coding ...

This paper investigates a non-negative matrix factorization (NMF)-based approach to the semi-supervised single-channel speech enhancement problem where only non-stationary additive noise signals are given. The proposed method relies on sinusoidal model of speech production which is integrated inside NMF framework using linear constraints on dictionary atoms. This method is further developed to ...

In this paper, we present an on-line semi-supervised algorithm for real-time separation of speech and background noise. The proposed system is based on Nonnegative Matrix Factorization (NMF), where fixed speech bases are learned from training data whereas the noise components are estimated in real-time on the recent past. Experiments with spontaneous conversational speech and real-life nonstati...

Given a vector space model encoding of a large data set, a usual starting point for data analysis is rank reduction [1]. However, standard rank reduction techniques such as the QR, Singular Value (SVD), and Semi-Discrete (SDD) decompositions and Principal Component Analysis (PCA) produce low rank bases which do not respect the non-negativity or structure of the original data. Non-negative Matri...