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A Turbo iterative method for signal processing is proposed. This method is a kind of multi-systems collaborative signal processing through iteration: several independent systems work in rotation, and each system takes feedback information from the other systems as a priori condition. We have applied such a Turbo iterative signal processing (TISP) method on speech signal enhancement, and on SAR (synthetic...
Much of modern machine learning and statistics research consists of extracting information from high-dimensional patterns. Often times, the large number of features that comprise this high-dimensional pattern are themselves vector valued, corresponding to sampled values in a time-series. Here, we present a classification methodology to accommodate multiple time-series using boosting. This method constructs...
Automatic modulation recognition is a topic of interest in many fields including signal surveillance, multi-user detection and radio frequency spectrum monitoring. A major weakness of conventional modulation recognition algorithms is their reliance on high SNR environments and favorable statistics. In this paper an algorithm is developed using elements of cyclo-spectral analysis, ICA and SVM algorithms...
Identification of assets on the stock market that exhibit co-movement is a critical task for generating an efficiently diversified portfolio. We present a new application of non-negative matrix factorization to factor analysis of financial time series. We consider a conditionally heteroscedastic latent factor model, where each series is parameterized by a univariate ARCH model. Volatility clustering...
Modern methods for nonlinear dimensionality reduction have been used extensively in the machine learning community for discovering the intrinsic dimension of several datasets. In this paper we apply one of the most successful ones maximum variance unfolding on a big sample of the well known speech benchmark TIMIT. Although MVU is not generally scalable, we managed to apply to 1 million 39-dimensional...
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