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A Cellular Automata mathematical model is proposed to analyze the depression spreading. A simulation is done and the results verified that the mathematical model is right and excellent. A linear regression model is presented to describe the relationship among prevalence rate of depression, incidence and cure rates. Principal Component Analysis conforms that any parameter is important in the linear...
Structured Support Vector Machine (SVM) is a recently developed extension of the very successful SVM approach, which can efficiently classify structured pattern with maximized margin. This paper presents an initial attempt for phoneme recognition using structured SVM. We simply learn the basic framework of HMMs in configuring the structured SVM. In the preliminary experiments with TIMIT corpus, the...
In this paper, we propose an efficient speaker clustering approach based on a locality preserving linear projective mapping in the Gaussian mixture model (GMM) mean supervector space. While the GMM mean supervector has turned out to be an effective representation of speakers, its dimensionality is usually very high. The locality preserving projection (LPP) maps the high-dimensional GMM mean supervector...
Speaker subspace modeling has become increasingly important in speaker recognition, diarization, and clustering. Principal component analysis (PCA) is a popular linear subspace learning technique and the approach that represents an arbitrary utterance or speaker as a linear combination of a set of basis voices based on PCA is known as the eigenvoice approach. In this paper, a novel technique, namely...
This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of features, and then a global Gaussian Mixture Model (GMM) learned from all images is used to randomly distribute each feature into one Gaussian component by a multinomial trial. The parameters of the multinomial distribution are...
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