We present an unsupervised, comprehensive methodology for the construction of financial risk models. We o↵er qualitative comments on incremental functionality and quantitative measures of superior performance of component and mixture dynamic linear models relative to alternative models. We apply our methodology to a high dimensional stream of daily closing prices for approximately 7,000 US trad...

Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to extract the local principal orientations in the data. An important issue with this generative model is its sensitivity to data lying off the low-dimensional manifold. In order to address this problem, the mixtures of r...

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regre...

We derive an asymptotic Newton algorithm for Quasi Maximum Likelihood estimation of the ICA mixture model, using the ordinary gradient and Hessian. The probabilistic mixture framework can accommodate non-stationary environments and arbitrary source densities. We prove asymptotic stability when the source models match the true sources. An application to EEG segmentation is given. Index Terms Ind...

Nonlinear PID design is difficult if one approaches the problem from a global design perspective. In this paper, we propose coalescing the adaptive local linear modeling approach with traditional linear PID controller design techniques to arrive at a principled, intuitive, and simple nonlinear PID controller design methodology. The paper, in particular, discusses two local linear modeling appro...

We propose the multivariate autoregressive model for content based music auto-tagging. At the song level our approach leverages the multivariate autoregressive mixture (ARM) model, a generative time-series model for audio, which assumes each feature vector in an audio fragment is a linear function of previous feature vectors. To tackle tagmodel estimation, we propose an efficient hierarchical E...

This paper provides a general technique for lower bounding the Bayes risk of statistical estimation, applicable to arbitrary loss functions and arbitrary prior distributions. A lower bound on the Bayes risk not only serves as a lower bound on the minimax risk, but also characterizes the fundamental limit of any estimator given the prior knowledge. Our bounds are based on the notion of f -inform...

To solve the task of segmenting clusters of nearly identical objects we here present the template rotation expectation maximization (TREM) approach which is based on a generative model. We explore both a non-linear optimization approach for maximizing the loglikelihood and a modification of the standard expectation maximization (EM) algorithm. The non-linear approach is strict template matching...

We give oracle inequalities on procedures which combines quantization and variable selection via a weighted Lasso k-means type algorithm. The results are derived for a general family of weights, which can be tuned to size the influence of the variables in different ways. Moreover, these theoretical guarantees are proved to adapt the corresponding sparsity of the optimal codebooks, suggesting th...