I am trying to find which classification methods, that do not use a training phase, are available.
The scenario is gene expression based classification, in which you have a matrix of gene expression of m genes (features) and n samples (observations). A signature for each class is also provided (that is a list of the features to consider to define to which class belongs a sample).
An application (non-training) is the Nearest Template Prediction method. In this case it is computed the cosine distance between each sample and each signature (on the common set of features). Then each sample is assigned to the nearest class (the sample-class comparison resulting in a smaller distance). No already classified samples are needed in this case.
A different application (training) is the kNN method, in which we have a set of already labeled samples. Then, each new sample is labeled depending on how are labeled the k nearest samples.
Are there any other non-training methods?