99,386 research outputs found
Semi-supervised cross-entropy clustering with information bottleneck constraint
In this paper, we propose a semi-supervised clustering method, CEC-IB, that
models data with a set of Gaussian distributions and that retrieves clusters
based on a partial labeling provided by the user (partition-level side
information). By combining the ideas from cross-entropy clustering (CEC) with
those from the information bottleneck method (IB), our method trades between
three conflicting goals: the accuracy with which the data set is modeled, the
simplicity of the model, and the consistency of the clustering with side
information. Experiments demonstrate that CEC-IB has a performance comparable
to Gaussian mixture models (GMM) in a classical semi-supervised scenario, but
is faster, more robust to noisy labels, automatically determines the optimal
number of clusters, and performs well when not all classes are present in the
side information. Moreover, in contrast to other semi-supervised models, it can
be successfully applied in discovering natural subgroups if the partition-level
side information is derived from the top levels of a hierarchical clustering
Pareto Improving Social Security Reform when Financial Markets are Incomplete!?
While much of classical statistical analysis is based on Gaussian distributional assumptions, statistical modeling with the Laplace distribution has gained importance in many applied fields. This phenomenon is rooted in the fact that, like the Gaussian, the Laplace distribution has many attractive properties. This paper investigates two methods of combining them and their use in modeling and predicting financial risk. Based on 25 daily stock return series, the empirical results indicate that the new models offer a plausible description of the data. They are also shown to be competitive with, or superior to, use of the hyperbolic distribution, which has gained some popularity in asset-return modeling and, in fact, also nests the Gaussian and Laplace.GARCH, Hyperbolic Distribution, Kurtosis, Laplace Distribution, Mixture Distributions, Stock Market Returns
Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models
Single-channel deep speech enhancement approaches often estimate a single
multiplicative mask to extract clean speech without a measure of its accuracy.
Instead, in this work, we propose to quantify the uncertainty associated with
clean speech estimates in neural network-based speech enhancement. Predictive
uncertainty is typically categorized into aleatoric uncertainty and epistemic
uncertainty. The former accounts for the inherent uncertainty in data and the
latter corresponds to the model uncertainty. Aiming for robust clean speech
estimation and efficient predictive uncertainty quantification, we propose to
integrate statistical complex Gaussian mixture models (CGMMs) into a deep
speech enhancement framework. More specifically, we model the dependency
between input and output stochastically by means of a conditional probability
density and train a neural network to map the noisy input to the full posterior
distribution of clean speech, modeled as a mixture of multiple complex Gaussian
components. Experimental results on different datasets show that the proposed
algorithm effectively captures predictive uncertainty and that combining
powerful statistical models and deep learning also delivers a superior speech
enhancement performance.Comment: 5 pages, 4 figure
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