Databases Reference
In-Depth Information
Chapter 7
Multi-Feature Classifications for
Complex Data
Pattern recognition involves a set of processes to define similarities and/or
differences between two or more patterns. Patterns or data must be evaluated
or measured to find distinctive characteristics. The first step in any pattern
recognition scheme is to identify measurable quantities or characteristics of
patterns that match a specific class of data. These measurable quantities are
known as features. According to Theodoridis and Koutroumbas [85], features
can be defined as a set of measurements used for recognition and classifica-
tion. These measurements form a feature vector that is used for recognition
purposes. In image recognition, examples of features include colors, edges, and
spectrum frequencies.
Pattern recognition, as described in the previous chapters, is a series of
processes including data acquisition, data pre-processing, and classification
[86]. Each data presented for recognition is assigned to the data class that most
closely matches the features of the data. These features are extracted before
any classification/recognition process takes place. The extraction process is
performed during the pre-processing stage of pattern recognition. In existing
pattern recognition schemes, the number of features used tends to be very
large. A phenomenon known as the “curse of dimensionality” arises as a result
of the high dimensionality of the computational space.
This chapter focuses on pattern recognition schemes involving multiple fea-
tures. A multiple-feature implementation enables a holistic approach to the
pattern recognition procedure that takes into consideration all significant fea-
tures representing a particular set of patterns, such as images and sensor read-
ings. This multi-feature consideration is important when considering complex
data in an Internet-scale environment. The multi-feature approach was de-
signed to reduce the bias effect caused by selecting only a single feature for
classification/recognition purposes. To avoid the curse of dimensionality, cur-
rent approaches in pattern recognition require a significant amount of effort
to analyze different forms of features. This effort limits their ability to seam-
lessly and effectively perform recognition and classification on complex data
sets. Furthermore, the computational complexity of most existing schemes
inhibits their ability to scale up to an increasing number of features.
It is envisioned that the distributed approach can be implemented in
Internet-scale pattern recognition involving multiple features. It is argued that
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