  In [1, 2], we have explored the theoretical aspects of feature extraction
optimization processes for solving largescale problems and overcoming machine
learning limitations. Majority of optimization algorithms that have been
introduced in [1, 2] guarantee the optimal performance of supervised learning,
given offline and discrete data, to deal with curse of dimensionality (CoD)
problem. These algorithms, however, are not tailored for solving emerging
learning problems. One of the important issues caused by online data is lack of
sufficient samples per class. Further, traditional machine learning algorithms
cannot achieve accurate training based on limited distributed data, as data has
proliferated and dispersed significantly. Machine learning employs a strict
model or embedded engine to train and predict which still fails to learn unseen
classes and sufficiently use online data. In this chapter, we introduce these
challenges elaborately. We further investigate Meta-Learning (MTL) algorithm,
and their application and promises to solve the emerging problems by answering
how autonomous agents can learn to learn?.
