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This is a common problem in combinatorial searches in materials science, as well as chemistry. However, these data-sets may well be \u2018small\u2019 in terms of limited step-size of the measurement variables. Due to this limitation, application of higher-order statistics is not effective, and the choice of a suitable unsupervised learning method is restricted to those utilizing lower-order statistics. As an interesting case study, we present here variable magnetic-field Piezoresponse Force Microscopy (PFM) study of composite multiferroics, where due to experimental limitations the magnetic field dependence of piezoresponse is registered with a coarse step-size. An efficient extraction of this dependence, which corresponds to the local magnetoelectric effect, forms the central problem of this work. We evaluate the performance of Principal Component Analysis (PCA) as a simple unsupervised learning technique, by pre-labeling possible patterns in the data using Density Based Clustering (DBSCAN). Based on this combinational analysis, we highlight how PCA using non-central second-moment can be useful in such cases for extracting information about the local material response and the corresponding spatial distribution.<\/jats:p>","DOI":"10.1038\/s41524-018-0084-9","type":"journal-article","created":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T12:33:16Z","timestamp":1529065996000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Sequential piezoresponse force microscopy and the \u2018small-data\u2019 problem"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1232-5825","authenticated-orcid":false,"given":"Harsh","family":"Trivedi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7155-2473","authenticated-orcid":false,"given":"Vladimir V.","family":"Shvartsman","sequence":"additional","affiliation":[]},{"given":"Marco S. 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