%0 Journal Article
	%A Takahiro Nishigaki and  Katsumi Nitta and  Takashi Onoda
	%D 2017
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 122, 2017
	%T Incremental Learning of Independent Topic Analysis
	%U https://publications.waset.org/pdf/10006461
	%V 122
	%X In this paper, we present a method of applying
Independent Topic Analysis (ITA) to increasing the number of
document data. The number of document data has been increasing
since the spread of the Internet. ITA was presented as one method
to analyze the document data. ITA is a method for extracting the
independent topics from the document data by using the Independent
Component Analysis (ICA). ICA is a technique in the signal
processing; however, it is difficult to apply the ITA to increasing
number of document data. Because ITA must use the all document
data so temporal and spatial cost is very high. Therefore, we
present Incremental ITA which extracts the independent topics from
increasing number of document data. Incremental ITA is a method
of updating the independent topics when the document data is added
after extracted the independent topics from a just previous the data.
In addition, Incremental ITA updates the independent topics when the
document data is added. And we show the result applied Incremental
ITA to benchmark datasets.
	%P 237 - 243