Less is more: Low-precision feature selection for wearables

Nowadays, the amount of data produced daily has significantly increased due to the growth in the number of wearable devices. Similarly, this increase is also visible in the interest of developing machine learning algorithms with reduced precision computations, due to the limitations of such devices. This work studies the effect of using low precision operations in the context of feature selection, a preprocessing step that is becoming necessary to deal with the increasing data dimensionality. This study focuses specifically on feature selection methods based on Mutual Information (one of the most popular and widely-used metrics in this area) and how low precision computations can be carried out obtaining experimental results similar to those achieved by double-precision over several low- and high-dimensional datasets. We observe that the use of 16-bit fixed-point representation makes it possible to obtain feature rankings with high similarity to those obtained in double-precision. Even the rankings obtained with 8 bits and then used in subsequent classification tasks, lead to similar accuracy (no significant difference) to the one obtained when using the 64-bit representation in certain situations.