Human Activity Recognition in AAL Environments Using Random Projections

Table 2

Summary of feature selection/dimensionality reduction methods in HAR.

Method	Advantages	Disadvantages	Complexity
PCA	High dimensionality reduction; reduction of noise; lack of redundancy of data due to orthogonality of components	The covariance matrix is difficult to be evaluated accurately; even the simplest invariance could not be captured by the PCA unless the training data explicitly provides for it	, where are data points, each represented with features
ReliefF	Low computational complexity	Unstable due to random selection of instances
Rankfeatures	Features highly correlated with already selected features are less likely to be included	It assumes that data classes are normally distributed	It depends upon class separability criterion
CFS	It evaluates a subset of features rather than individual features	It fails to select locally predictive features when they are overshadowed by strong, globally predictive features