Digital signal processing (2nd ed.): principles, algorithms, and applications
Digital signal processing (2nd ed.): principles, algorithms, and applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
An optimal polygonal boundary encoding scheme in the rate distortion sense
IEEE Transactions on Image Processing
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This paper presents a linear frequency estimation (LFE) technique for data reduction of frequency-based signals. LFE converts a signal to the frequency domain by utilizing the Fourier transform and estimates both the real and imaginary parts with a series of vectors much smaller than the original signal size. The estimation is accomplished by selecting optimal points from the frequency domain and interpolating data between these points with a first order approximation. The difficulty of such a problem lies in determining which points are most significant. LFE is unique in the fact that it is generic to a wide variety of frequency-based signals such as electromyography (EMG), voice, and electrocardiography (ECG). The only requirement is that spectral coefficients are spatially correlated. This paper presents the algorithm and results from both EMG and voice data. We complete the paper with a description of how this method can be applied to pattern types of recognition, signal indexing, and compression.