  During the last decade, the deluge of multimedia data has impacted a wide
range of research areas, including multimedia retrieval, 3D tracking, database
management, data mining, machine learning, social media analysis, medical
imaging, and so on. Machine learning is largely involved in multimedia
applications of building models for classification and regression tasks etc.,
and the learning principle consists in designing the models based on the
information contained in the multimedia dataset. While many paradigms exist and
are widely used in the context of machine learning, most of them suffer from
the `curse of dimensionality', which means that some strange phenomena appears
when data are represented in a high-dimensional space. Given the high
dimensionality and the high complexity of multimedia data, it is important to
investigate new machine learning algorithms to facilitate multimedia data
analysis. To deal with the impact of high dimensionality, an intuitive way is
to reduce the dimensionality. On the other hand, some researchers devoted
themselves to designing some effective learning schemes for high-dimensional
data. In this survey, we cover feature transformation, feature selection and
feature encoding, three approaches fighting the consequences of the curse of
dimensionality. Next, we briefly introduce some recent progress of effective
learning algorithms. Finally, promising future trends on multimedia learning
are envisaged.
