Real-world datasets, such as genomic data, are noisy and high-dimensional, and are therefore difficult to analyse without a preliminary step aimed to reduce data dimensionality and to select relevant features. Projection techniques are a useful tool to pre-process high dimensional datd since they allow to achieve a simpler representation of the original data that still preserves intrinsic information. In this work, we assess the effectiveness of these methods when applied to two common tasks in Bioinformatics: patient classification and gene clustering. We compared the performance of different learning models in the original space and in several projected spaces obtained with different techniques, both in a supervised and in an unsupervised setting. Our results show that projection techniques can lead to a significant improvement in the learning ability of models.
|Title of host publication||2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 9 Nov 2016|
|Event||2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016 - Bologna, Italy|
Duration: 7 Sep 2016 → 9 Sep 2016
|Conference||2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016|
|Period||7/09/16 → 9/09/16|