We define a topic modeling to partition documents into associated topics with nonnegative matrix factorization (NMF). We apply classical approach and also Bayesian inference with Kullback-Leibler error measure for approximating the decompositions of the matrix. As a baseline, we use topic model with LDA to compare it to our model. We evaluate our proposed method by defining domain specific metrics such as topic-uniqueness and overall topic-uniqueness. With our topic modeling approach, we provide efficient processing of large collections while preserving the essential statistical relationships.
In order to reveal similarities and differences in time-series, finding underlying hidden trends, can be thought as all trends which are explaining a whole timeseries set, is very important. In the project, with the help of decompose a mixture data via Non-negative matrix factorization (NMF), time series into its constitute parts, the underlying trends extraction is tried to achieve. After NMF reveals underlying trends (bases), resulting encodings become prepared for clustering with k-Means algorithm.