ABSTRACT
Multilinear subspace learning (MSL) is very useful for a wide range of application domains that involve tensor-structured datasets. This final chapter starts with an introduction to a typical pattern recognition system as shown in Figure 9.1, and then examines real-world applications of MSL. In particular, we study two biometric applications in greater detail: face recognition and gait recognition, due to their popularity in the development of MSL. Some experimental results are presented for demonstration. For completeness sake, several other applications are also described briefly, such as visual content analysis in computer vision, brain signal/image processing in neuroscience, DNA sequence discovery in bioinformatics, music genre classification in audio signal processing, and data stream monitoring in data mining.
