In this paper we study the use of unsupervised feature learning for acoustic scene classification (ASC). The acoustic environment recordings are represented by time-frequency images from which we learn features in an unsupervised manner. After a set of preprocessing and pooling steps, the images are decomposed using matrix factorization methods. By decomposing the data on a learned dictionary, we use the projection coefficients as features for classification. An experimental evaluation is done on a large ASC dataset to study popular matrix factorization methods such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) as well as some of their extensions including sparse, kernel based and convolutive variants. The results show the compared variants lead to significant improvement compared to the state-of-the-art results in ASC.