Random Projection, Margins, Kernels, and Feature-Selection

Random projection is a simple technique that has had a number of applications in algorithm design. In the context of machine learning, it can provide insight into questions such as “why is a learning problem easier if data is separable by a large margin?” and “in what sense is choosing a kernel much like choosing a set of features?” This talk is

• Type: Conference
• Year: 2005
• Where: SLSFS
• Authors: Avrim Blum
• Keywords: Algorithm Design, Pattern Recognition, Random Projection, Simple Technique, SLSFS 2005