On Computational Issues of Semi-Supervised Local Fisher Discriminant Analysis
نویسنده
چکیده
Dimensionality reduction is one of the important preprocessing steps in practical pattern recognition. SEmi-supervised Local Fisher discriminant analysis (SELF)— which is a semi-supervised and local extension of Fisher discriminant analysis—was shown to work excellently in experiments. However, when data dimensionality is very high, a naive use of SELF is prohibitive due to high computational costs and large memory requirement. In this paper, we introduce computational tricks for making SELF applicable to large-scale problems.
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ورودعنوان ژورنال:
- IEICE Transactions
دوره 92-D شماره
صفحات -
تاریخ انتشار 2009