Computational Intelligence - August 2014 - 38

scalable computational implementations of the methods in
order to process the TDA databases within feasible times, and
finally, 5) the sometimes difficult interpretation of the results
obtained and the question of how to gain physical insight
from them.
The quality of a training set is critical for the correct performance of supervised methods. In astronomy an intrinsic
sample selection bias occurs when knowledge gathered from
previous surveys is used with new data. Semi-supervised learning and active learning rise as feasible options to cope with
large and heterogeneous astronomical data, providing particular
solutions to the dilemmas regarding training sets. It is very
likely that we will see more semi-supervised applications for
astronomy in the near future. The reuse of training sets is critical in terms of scalability and validity of results. The integration
with existing databases and the incorporation of data observed
at different wavelengths are currently open issues. Feature
spaces that are survey-independent may provide an indirect
solution to the combination of training sets and the applications of trained classifiers across different surveys.
Although powerful, the sometimes extended calibration
required by machine learning methods can be difficult for
inexperienced users. The selection of the algorithms, the complexity of the implementations, the exploration of parameter
space, and the interpretation of the outputs in physical terms
are some of the issues one has to face when using machine
learning methods. The learning curve might be too steep for an
astronomer to take the initiative, but all the issues named here
can be solved by inter-disciplinary collaboration. Teams assembled from the fields of astronomy, statistics, computer science
and engineering have everything that is needed to propose
solutions for data-intensive TDA. The deluge of astronomical
data opens up huge opportunities for professionals with knowledge in computational intelligence and machine learning.
VI. Acknowledgment

This work was funded by CONICYT-CHILE under grant
FONDECYT 1110701 and 1140816, and its Doctorate Scholarship program. Pablo Estévez acknowledges support from the
Ministry of Economy, Development, and Tourism's Millennium
Science Initiative through grant IC12009, awarded to The Millennium Institute of Astrophysics, MAS.
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