  High dimension, low sample size (HDLSS) problems are numerous among
real-world applications of machine learning. From medical images to text
processing, traditional machine learning algorithms are usually unsuccessful in
learning the best possible concept from such data. In a previous work, we
proposed a dissimilarity-based approach for multi-view classification, the
Random Forest Dissimilarity (RFD), that perfoms state-of-the-art results for
such problems. In this work, we transpose the core principle of this approach
to solving HDLSS classification problems, by using the RF similarity measure as
a learned precomputed SVM kernel (RFSVM). We show that such a learned
similarity measure is particularly suited and accurate for this classification
context. Experiments conducted on 40 public HDLSS classification datasets,
supported by rigorous statistical analyses, show that the RFSVM method
outperforms existing methods for the majority of HDLSS problems and remains at
the same time very competitive for low or non-HDLSS problems.
