Enhancing Feature Extraction through G-PLSGLR by Decreasing Dimensionality of Textual Data

The technology of big data has become highly popular in numerous industries owing to its various characteristics such as high value, large volume, rapid velocity, wide variety, and significant variability. Nevertheless, big data presents several difficulties that must be addressed, including lengthy processing times, high computational complexity, imprecise features, significant sparsity, irrelevant terms, redundancy, and noise, all of which can have an adverse effect on the performance of feature extraction. The objective of this research is to tackle these issues by utilizing the Partial Least Square Generalized Linear Regression (G-PLSGLR) approach to decrease the high dimensionality of text data. The suggested algorithm is made up of four stages: Firstly, gathering featured data in vector space model (VSM) and training it with bootstrap technique. Second, grouping trained feature samples using a Pearson correlation coefficient and graph-based technique. Third, getting rid of unimportant features by ranking significant group features using PLSGR. Lastly, choosing or extracting significant features using Bayesian information criterion (BIC). The G-PLSGLR algorithm surpasses current methods by achieving a high reduction rate and classification performance, while minimizing feature redundancy, time consumption, and complexity. Furthermore, it enhances the accuracy of features by 35%

Enhanced signatures for event classification: The projector approach

Conference PaperThe classification of nonstationary signals of unknown duration is of great importance in areas like oil exploration, moving target detection, and pattern recognition. In an earlier work, we provided a solution to this problem, based on the wavelet transform, by defining representations called <i>pseudo power signatures</i> for signal classes which were independent of signal length, and proposed a simple approach using the Singular Value Decomposition to generate these signatures. This paper offers a new approach resulting in more discriminating signatures. The enhanced signatures are obtained by solving a nonlinear minimization problem involving an inverse projection. The problem formulation, solution procedure, and computational algorithm are presented in this work. The efficacy of the projection signatures in separating highly correlated signal classes is demonstrated through a simulation example

Nonstationary signal classification using pseudo power signatures: The Matrix SVD Approach

Journal PaperThis paper deals with the problem of classification of nonstationary signals using signatures which are essentially independent of the signal length. This independence is a requirement in common classification problems like stratigraphic analysis, which was a motivation for this research. We achieve this objective by developing the notion of an approximation to the Continuous Wavelet Transform (CWT), which is separable in the time and scale parameters, and using it to define <b>power signatures</b>, which essentially characterize the scale energy density, independent of time. We present a simple technique which uses the Singular Value Decomposition (SVD) to compute such an approximation, and demonstrate through an example how it is used to perform the classification process. The proposed classification approach has potential applications in areas like moving target detection, object recognition, oil exploration, and speech processing

Optimal parallel 2-D FIR digital filter with separable terms

Journal PaperThis paper completely solves the optimal Weighted Least Mean Square (WLMS) design problem using sums of separable terms. For any fixed number of separable terms (less than or equal to the rank of the unconstrained solution), the problem is solved as a sequence of separable filter approximations. An efficient computational algorithm based on necessary conditions is presented. The procedure allows a high degree of flexibility in the choice of filter orders and the number of separable terms, but it may converge to a local minimum. An improved approximation can be obtained by computing more terms than required and then performing a truncation of the coefficient matrix using a singular value analysis. A significant computational advantage is that the procedure requires neither the solution of the unconstrained WLMS problem nor the singular value analysis of the ideal filter

Nonstationary Signal Enhancement Using The Wavelet Transform

Conference PaperConventional signal processing typically involves frequency selective techniques which are highly inadequate for nonstationary signals. In this paper, we present an approach to perform time-frequency selective processing using the Wavelet Transform. The approach is motivated by the excellent localization, in both time and frequency, afforded by the wavelet basis functions. Suitably chosen wavelet basis functions are used to characterize the subspace of signals that have a given localized time-frequency support, thus enabling a time-frequency partitioning of signals. A practical implementation scheme using filter banks is also presented, and the effectiveness of the approach over conventional techniques is demonstrated

Nonstationary signal classification using pseudo power signatures

Conference PaperThis paper deals with the problem of classification of nonstationary signals using signatures which are essentially independent of the signal length. We develop the notion of a separable approximation to the Continuous Wavelet Transform (CWT) and use it to define a power signature. We present a simple technique which uses the Singular Value Decomposition (SVD) to compute such an approximation, and demonstrate through an example how it is used to perform the classification process. This example serves to show both the effectiveness and limitations of the approach. Our main result is an alternate approach which develops the idea of using orthogonal projections to refine the approximation process, thus allowing for the definition of better signatures

Enhanced Pseudo Power Signatures for Nonstationary Signal Classification: The Projector Approach

Journal PaperThe classification of nonstationary signals of unknown duration is of great importance in areas like oil exploration, moving target detection, and pattern recognition. In an earlier work, we provided a solution to this problem, based on the wavelet transform, by defining representations called <i>pseudo power signatures</i> for signal classes which were independent of signal length, location and magnitude, and proposed a simple approach using the Singular Value Decomposition to generate these signatures. This paper offers a new approach resulting in more discriminating signatures. The enhanced signatures are obtained by solving a nonlinear minimization problem involving an inverse projection. The problem formulation, solution procedure, and computational algorithm are presented in this work. An analysis of the projection signatures, and their efficacy in separating highly correlated signal classes are demonstrated through simulation examples

Detecting Periodic Behavior in Nonstationary Signals

Conference PaperThis paper presents results on the multiresolution analysis of nonstationary signals with the objective of detecting underlying periodic phenomena. Wavelet packet analysis with coefficient thresholding is the basis for the detection. The effectiveness of the method is illustrated by analyzing experimental data on sediment electrochemical redox potential in a tidal microcosm. The significance of the technique is that it can extract periodic phenomena from experimental data corrupted by catastrophic and random events, provide a signature of the basic periodic component, an give an estimate of the degree of deviation from periodic behavior. Consequently, it has potential applications in the analysis of quasi-periodic signals such as electrocardiograms (ECGs), where the determination of the extent of quasi-periodicity is of critical importance

Pseudo Power Signatures For Nonstationary Signal Analysis And Classification

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : xi Chapter 1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.1 Need for time independent signatures : : : : : : : : : : : : : : : : : : 2 1.1.1 Modern classification approaches : : : : : : : : : : : : : : : : 4 1.2 Overview of work : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 2 Pseudo Power Signatures : : : : : : : : : : : : : : : : : : : : : : : : 8 2.1 Time-frequency Distributions (TFD) : : : : : : : : : : : : : : : : : : 8 2.1.1 The Wigner Distribution : : : : : : : : : : : : : : : : : : : : : 9 2.1.2 The Short Time Fourier Transform : : : : : : : : : : : : : : : 11 2.1.3 The Continuous Wavelet Transform : : : : : : : : : : : : : : : 12 2.2 Time independent signatures : : : : : : : : : : : : : : : : : : : : : : : 17 2.2.1 Approximate power signatures : : : : : : : : : : : : : : : : : : 19 3 The Matrix SVD Approach : : : : : : : : : : : : : : : : : : : : : : ..

Detecting Periodic Behavior In Nonstationary Signals

This paper presents results on the multiresolution analysis of nonstationary signals with the objective of detecting underlying periodic phenomena. Wavelet packet analysis with coefficient thresholding is the basis for the detection. The effectiveness of the method is illustrated by analyzing experimental data on sediment electrochemical redox potential in a tidal microcosm. The significance of the technique is that it can extract periodic phenomena from experimental data corrupted by catastrophic and random events, provide a signature of the basic periodic component, and give an estimate of the degree of deviation from periodic behavior. Consequently, it has potential applications in the analysis of quasi-periodic signals such as electrocardiograms (ECGs), where the determination of the extent of quasi-periodicity is of critical importance. 1. INTRODUCTION In this paper, we aim to analyze experimental data relating to periodic phenomena which have been corrupted by catastrophic or r..