Extracting relevant features from signals is important for signal analysis such as
compression, noise removal, classification, or regression (prediction). Often, important …

We describe an extension to thebest-basis' method to construct an orthonormal basis which
maximizes a class separability for signal classification problems. This algorithm reduces the …

Signal analysis involves identifying signal behaviour, extracting linear and non-linear
properties, compression or expansion into higher or lower dimensions, and recognizing …

We describe an extension to the “best-basis” method to select an orthonormal basis suitable
for signal/image classification problems from a large collection of orthonormal bases …

A survey of computer algorithms and philosophies applied to problems of feature extraction
and pattern recognition in conjunction with image analysis is presented. The main emphasis …

Signal processing can be found in many applications and its primary goal is to provide
underlying information on specific problems for the purpose of decision making. Traditional …

This chapter introduces the reader to the various aspects of feature extraction covered in this
book. Section 1 reviews definitions and notations and proposes a unified view of the feature …

Pattern analysis often requires a pre-processing stage for extracting or selecting features in
order to help the classification, prediction, or clustering stage discriminate or represent the …

Feature engineering plays a vital role in big data analytics. Machine learning and data
mining algorithms cannot work without data. Little can be achieved if there are few features …

This paper presents a new concept of decomposition of a signal into component periodic
waveforms. The singular value decomposition (SVD) is used for the detection of periodicity …