Robust Regression (original) (raw)
2012, Lecture Notes in Computer Science
Discriminative methods (e.g., kernel regression, SVM) have been extensively used to solve problems such as object recognition, image alignment and pose estimation from images. Regression methods typically map image features (X) to continuous (e.g., pose) or discrete (e.g., object category) values. A major drawback of existing regression methods is that samples are directly projected onto a subspace and hence fail to account for outliers which are common in realistic training sets due to occlusion, specular reflections or noise. It is important to notice that in existing regression methods, and discriminative methods in general, the regressor variables X are assumed to be noise free. Due to this assumption, discriminative methods experience significant degrades in performance when gross outliers are present.
Sign up for access to the world's latest research.
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.