PMID- 18482473
OWN - NLM
STAT- MEDLINE
DCOM- 20080822
LR  - 20191210
IS  - 0007-1102 (Print)
IS  - 0007-1102 (Linking)
VI  - 61
IP  - Pt 1
DP  - 2008 May
TI  - Feature selection in feature network models: finding predictive subsets of 
      features with the Positive Lasso.
PG  - 1-27
LID - 10.1348/000711006X119365 [doi]
AB  - A set of features is the basis for the network representation of proximity data 
      achieved by feature network models (FNMs). Features are binary variables that 
      characterize the objects in an experiment, with some measure of proximity as 
      response variable. Sometimes features are provided by theory and play an 
      important role in the construction of the experimental conditions. In some 
      research settings, the features are not known a priori. This paper shows how to 
      generate features in this situation and how to select an adequate subset of 
      features that takes into account a good compromise between model fit and model 
      complexity, using a new version of least angle regression that restricts 
      coefficients to be non-negative, called the Positive Lasso. It will be shown that 
      features can be generated efficiently with Gray codes that are naturally linked 
      to the FNMs. The model selection strategy makes use of the fact that FNM can be 
      considered as univariate multiple regression model. A simulation study shows that 
      the proposed strategy leads to satisfactory results if the number of objects is 
      less than or equal to 22. If the number of objects is larger than 22, the number 
      of features selected by our method exceeds the true number of features in some 
      conditions.
FAU - Frank, Laurence E
AU  - Frank LE
AD  - Department of Methodology and Statistics, Utrecht University, The Netherlands. 
      L.E.Frank@uv.nl
FAU - Heiser, Willem J
AU  - Heiser WJ
LA  - eng
PT  - Journal Article
PL  - England
TA  - Br J Math Stat Psychol
JT  - The British journal of mathematical and statistical psychology
JID - 0004047
SB  - IM
MH  - *Algorithms
MH  - *Computer Graphics
MH  - Computer Simulation
MH  - Humans
MH  - *Least-Squares Analysis
MH  - Linear Models
MH  - *Neural Networks, Computer
MH  - Phonation
MH  - Phonetics
MH  - Speech Acoustics
MH  - Speech Perception
EDAT- 2008/05/17 09:00
MHDA- 2008/08/23 09:00
CRDT- 2008/05/17 09:00
PHST- 2008/05/17 09:00 [pubmed]
PHST- 2008/08/23 09:00 [medline]
PHST- 2008/05/17 09:00 [entrez]
AID - 10.1348/000711006X119365 [doi]
PST - ppublish
SO  - Br J Math Stat Psychol. 2008 May;61(Pt 1):1-27. doi: 10.1348/000711006X119365.