Recently, the so-called Support Feature Machine (SFM) was proposed as a novel approach to feature selection for classification. It relies on approximating the zero-norm minimising weight vector of a separating hyperplane by optimising for its one-norm. In contrast to the L1-SVM it uses an additional constraint based on the average of data points. In experiments on artificial datasets we observe that the SFM is highly superior in returning a lower number of features and a larger percentage of truly relevant features. Here, we derive a necessary condition that the zero-norm and 1-norm solution coincide. Based on this condition the superiority can be made plausible.
|Title of host publication||Artificial Neural Networks and Machine Learning – ICANN 2011|
|Editors||Timo Honkela, Włodzisław Duch, Mark Girolami, Samuel Kaski|
|Number of pages||8|
|Publisher||Springer Berlin Heidelberg|
|Publication status||Published - 2011|
|Event||21st International Conference on Artificial Neural Networks - Espoo, Finland|
Duration: 14.06.2011 → 17.06.2011