Fast Sparse Multinomial Regression Applied to Hyperspectral Data
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چکیده
Author(s): Borges JS (Borges, Janete S.), Bioucas-Dias JM (Bioucas-Dias, Jose M.), Marcal ARS (Marcal, Andre R. S.) Source: IMAGE ANALYSIS AND RECOGNITION, PT 2 Book Series: LECTURE NOTES IN COMPUTER SCIENCE Volume: 4142 Pages: 700709 Published: 2006 Times Cited: 0 References: 15 Citation Map Abstract: Methods for learning sparse classification are among the state-of-the-art in supervised learning. Sparsity, essential to achieve good generalization capabilities, can be enforced by using heavy tailed priors/regularizers on the weights of the linear combination of functions. These priors/regularizers favour a few large weights and many to exactly zero. The Sparse Multinomial Logistic Regression algorithm [1] is one of such methods, that adopts a Laplacian prior to enforce sparseness. Its applicability to large datasets is still a delicate task from the computational point of view, sometimes even impossible to perform. This work implements an iterative procedure to calculate the weights of the decision function that is O(m(2)) faster than the original method introduced in [1] (m is the number of classes). The benchmark dataset Indian Pines is used to test this modification. Results over subsets of this dataset are presented and compared with others computed with support vector machines. Document Type: Article Language: English Reprint Address: Borges, JS (reprint author), Univ Porto, Fac Ciencias, DMA, Rua Campo Alegre,687, P-4169007 Oporto, Portugal Addresses: 1. Univ Porto, Fac Ciencias, DMA, P-4169007 Oporto, Portugal 2. Inst Super Tecn, Inst Telecommun, Lisbon, Portugal E-mail Addresses: [email protected], [email protected], [email protected] Publisher: SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY Subject Category: Computer Science, Theory & Methods IDS Number: BFF16 ISSN: 0302-9743 Cited by: 0 This article has been cited 0 times (from Web of Science).
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