Massive Data Sets: Proceedings of a Workshop by National Research Council, Division on Engineering and

By National Research Council, Division on Engineering and Physical Sciences, Mathematics, and Applications Commission on Physical Sciences, Committee on Applied and Theoretical Statistics

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Sample text

SVD is closely related to Eigen Decomposition, Factor Analysis, Principle Components Analysis, and Linear Neural Nets. We use the truncated SVD to approximate the term-by-document matrix using a smaller number of statistically derived orthogonal indexing dimensions. Roughly speaking, these dimensions can be thought of as artificial concepts representing the extracted common meaning components of many different terms and documents. We use this reduced representation rather than surface level word overlap for retrieval.

The subject practices each of the two tasks for about a minute each, responding to the task, for example, by pushing a button with the right thumb. The subject performs one of the tasks repeatedly while images are recorded and then switches to the other task. In some of our smaller experiments we are recording 100 images for each task. In order to eliminate left-right effects the entire experiment is repeated with the subject using the left thumb to respond. Thus there are a total of 400 images in this simple experiment.

An actual experiment might proceed as follows. A subject lies in the MRI magnet with a head restraint intended to minimize movement. A set of preliminary anatomical images are studied to determine the location within the brain where the repeated functional images will be taken. The subject practices each of the two tasks for about a minute each, responding to the task, for example, by pushing a button with the right thumb. The subject performs one of the tasks repeatedly while images are recorded and then switches to the other task.

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