[BibTeX] [RIS]
Spectral Dimensionality Reduction
Type of publication: Incollection
Citation: Bengio+al-spectral-2006
Booktitle: Feature Extraction, Foundations and Applications
Year: 2006
Publisher: Springer
URL: http://www.iro.umontreal.ca/~l...
Abstract: In this chapter, we study and put under a common framework a number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian eigenmaps and kernel PCA, which are based on performing an eigen-decomposition (hence the name "spectral"). That framework also includes classical methods such as PCA and metric multidimensional scaling (MDS). It also includes the data transformation step used in spectral clustering. We show that in all of these cases the learning algorithm estimates the principal eigenfunctions of an operator that depends on the unknown data density and on a kernel that is not necessarily positive semi-definite. This helps to generalize some of these algorithms so as to predict an embedding for out-of-sample examples without having to retrain the model. It also makes it more transparent what these algorithm are minimizing on the empirical data and gives a corresponding notion of generalization error.
Userfields: cat={B},topics={HighDimensional,Kernel,Unsupervised},
Keywords:
Authors Bengio, Yoshua
Delalleau, Olivier
Le Roux, Nicolas
Paiement, Jean-François
Vincent, Pascal
Ouimet, Marie
Editors Guyon, Isabelle
Gunn, Steve
Nikravesh, Masoud
Zadeh, Lofti
Added by: [ADM]
Total mark: 0
Attachments
  • eigenfn_chapter.pdf
  • http://www.iro.umontreal.ca/~l...
Notes
    Topics