Information Technology Reference
In-Depth Information
Fig. 4. Traditional wavelet decomposition
2. Technical overview
2.1 Wavelets
Wavelets are mathematical tools that are used to decompose/transform data into different
components (coefficients) that describe different levels of detail (Lahm, 2002). Thus, they can
extract the main features of a signal while simultaneously and independently analyzing
details.
These tools have been applied to several problems, including the challenges of linguistic
information retrieval. For example, wavelets have been used to build a Fuzzy Wavelet
Neural Network (FWNN) for decision making over multiple criteria (Chen, 2008). In that
analysis, custom built linguistic labels were used to represent information about events and
situations and were processed with the FWNN.
Wavelets are sometimes used to replace linguistic analysis. For example, Tolba (Tolba, 2005)
used consonant and vowel segmentation to develop automatic speech recognition for Arabic
speech without linguistic information. Segmentation was performed with a combination of
wavelet transformation and spectral analysis.
Hui and Wanglu combined the Linguistic Cloud Model (LCM) with wavelets to produce
Advanced Synthetic Aperture Radar (ASAR) image target detection (Hui, 2008). This
approach first solves image segmentation, avoids noise and recovers errors. Then, it uses
LCM to solve the uncertainty of pixels. Representation using LCM bridges the gap between
qualitative knowledge and quantitative knowledge, and it is thus used to map linguistic
terms with contextually specific meaning to numeric processing.
2.2 Comparison between MLW and traditional wavelets
To demonstrate the concept of MLW and its relationship to its traditional counterpart, this
table summarizes the main characteristics that unite or distinguish them:
