ISBN:
9781450328814
Content:
Dimensionality reduction is an important problem class in machine learning and data mining, as the dimensionality of data sets is steadily increasing. This work is a contribution in the line of research on iterative unsupervised kernel regression (UKR), a class of methods for dimensionality reduction that employ regression methods to find low-dimensional representations of high-dimensional patterns. We introduce a hybrid optimization approach of iteratively constructing a solution and performing gradient descent in the data space reconstruction error (DSRE). Further, we introduce a variable kernel function that increases the flexibility of UKR learning. The variable kernel function increases the model capacity, but introduces new parameters that have to be tuned.
In:
Igel, Christian, Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, New York, NY : ACM, 2014, (2014), Seite 77-78, 9781450328814
In:
year:2014
In:
pages:77-78
Language:
English
DOI:
10.1145/2598394.2598459
URL:
https://doi.org/10.1145/2598394.2598459
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