Name
Abstract | ||
|---|---|---|
We present an approach to learn predictive models and perform variable selection by incorporating structural information from Mass Spectral Imaging (MSI) data. We explore the use of a smooth quadratic penalty to model the natural ordering of the physical variables, that is the mass-to-charge (m/z) ratios. Thereby, estimated model parameters for nearby variables are enforced to smoothly vary. Similarly, to overcome the lack of labeled data we model the spatial proximity among spectra by means of a connectivity graph over the set of predicted labels. We explore the usefulness of this approach in a mouse brain MSI data set. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16001-1_28 | PRIB |
Keywords | Field | DocType |
semi-supervised learning,mass spectral imaging,sparse linear model,connectivity graph,physical variable,smooth quadratic penalty,mouse brain msi data,predictive model,estimated model parameter,nearby variable,structural information,spatial proximity,linear model,sparsity,convex optimization,spatial information,variable selection,prediction model,regularization,graph laplacian,connected graph,spectral imaging,semi supervised learning | Spatial analysis,Laplacian matrix,Spectral imaging,Semi-supervised learning,Feature selection,Pattern recognition,Computer science,Linear model,Regularization (mathematics),Artificial intelligence,Convex optimization,Machine learning | Conference |
Volume | ISSN | ISBN |
6282 | 0302-9743 | 3-642-16000-X |
Citations | PageRank | References |
2 | 0.72 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Ojeda | 1 | 65 | 4.60 |
| Marco Signoretto | 2 | 155 | 9.10 |
| Raf Van de Plas | 3 | 55 | 3.67 |
| Etienne Waelkens | 4 | 7 | 2.06 |
| Bart De Moor | 5 | 5541 | 474.71 |
| Johan A K Suykens | 6 | 2346 | 241.14 |