TRANSFORMING MULTIDIMENSIONAL DATA INTO IMAGES TO OVERCOME THE CURSE OF DIMENSIONALITY

When dealing with high-dimensional multivariate time series classification problems, a well-known difficulty is the curse of dimensionality. In this article, we propose an original approach of transposition of multidimensional data into images to tackle the task of classification. We propose a small hybrid model containing convolutional layers as a feature extractor followed by a recurrent neural network that take this transposed data as an input. We apply our method to a large dataset consisting of individual patient medical records. We show that our approach allows us to significantly reduce the size of a network and increase its performance by opting for a transformation of the input data.