Using Unsupervised Learning with Independent Component Analysis to Identify Patterns of Glaucomatous Visual Field Defects

Author:

Goldbaum Michael H.1,Sample Pamela A.2,Zhang Zuohua3,Chan Kwokleung3,Hao Jiucang3,Lee Te-Won3,Boden Catherine2,Bowd Christopher2,Bourne Rupert2,Zangwill Linda2,Sejnowski Terrence4,Spinak David2,Weinreb Robert N.2

Affiliation:

1. From the Ophthalmic Informatics Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, and the2Veterans Administration San Diego Health Services, San Diego, California; and the

2. From the Ophthalmic Informatics Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, and the

3. Institute for Neural Computation, University of California at San Diego, La Jolla, California; the

4. Computational Neurobiology Laboratory, Salk Institute, La Jolla, California.

Publisher

Association for Research in Vision and Ophthalmology (ARVO)

Subject

General Medicine

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