I unsuccessfully tried to resolve the issue with dimensions in NMF. The main problem that I was facing was with specifying the correct dimensions for matrix operations within NMF algorithm and I have yet to identify the correct dimensions. Furthermore, there is no incremental algorithms for NMF as in PCA. Thus, if a large data is given, it becomes infeasible to perform NMF. There are several heuristics to go around this issue with scalability, namely parallel NMF, which divides columns (i.e. features) of the data matrix into several subsets, perform NMF independently on each of the column subsets, and then join the results at the end. Unfortunately, this algorithm was not feasible for LiberTEM because with LiberTEM, one only has access to the rows of the data matrix (i.e., images) and not to the full columns (i.e., feature vectors) at each step. I also tried to clean up jupyter notebook and reorganize.

**What did I do this week?**

Work on NMF

**What will I work on next week?**

Write documentation