Regularized information preserving projections

Jing Peng, Alex J. Aved

Research output: Contribution to journalArticlepeer-review


In hyperspectral data classification, too many bands cause overfitting. Techniques for reducing bands are either informative or discriminant. Each technique has its strengths and weaknesses. Motivated by Gaussian processes latent variable models, we propose a linear projection technique that is both informative and discriminant. The technique optimizes a regularized information preserving objective, where regularization sets a preference for latent variables. Experimental results based on hyperspectral image data are provided to validate the proposed technique.

Original languageEnglish
Pages (from-to)3868-3877
Number of pages10
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number5
StatePublished - Oct 2020

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