Name
Abstract | ||
|---|---|---|
It is well known that supervised learning problems with ℓ1 (Lasso) and ℓ2 (Tikhonov or Ridge) regularizers will result in very different solutions. For example, the ℓ1 solution vector will be sparser and can potentially be used both for prediction and feature selection. However, given a data set it is often hard to determine which form of regularization is more applicable in a given context. In this paper we use mathematical properties of the two regularization methods followed by detailed experimentation to understand their impact based on four characteristics: non-stationarity of the data generating process, level of noise in the data sensing mechanism, degree of correlation between dependent and independent variables and the shape of the data set. The practical outcome of our research is that it can serve as a guide for practitioners of large scale data mining and machine learning tools in their day-to-day practice. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICTAI.2013.122 | ICTAI |
Keywords | Field | DocType |
ridge regularizers,day-to-day practice,data generating process,machine learning tools,classification,ℓ1 solution vector,tikhonov regularization,nonstationarity characteristics,learning (artificial intelligence),independent variables,lasso regularizers,tikhonov regularizers,lasso regularization,data sensing mechanism,correlation degree,impact basedon,prediction,data set shape,large scale data mining,noise level,lasso,guide forpractitioners,supervised learning problems,different solution,data mining,mathematical properties,detailed experimentation,feature selection,regularization methods,data sensingmechanism,regularization,correlation methods,supervised learning problem,learning artificial intelligence | Tikhonov regularization,Semi-supervised learning,Pattern recognition,Feature selection,Elastic net regularization,Computer science,Lasso (statistics),Supervised learning,Proximal gradient methods for learning,Artificial intelligence,Machine learning,Regularization perspectives on support vector machines | Conference |
ISSN | ISBN | Citations |
1082-3409 | 978-1-4799-2971-9 | 1 |
PageRank | References | Authors |
0.39 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Wang | 1 | 241 | 51.35 |
| Sanjay Chawla | 2 | 1372 | 105.09 |
| Wei Liu | 3 | 33 | 7.79 |