Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios

Modes and ridges of the probability density function behind observed data are useful geometric features. Mode-seeking clustering assigns cluster labels by associating data samples with the nearest modes, and estimation of density ridges enables us to find lower-dimensional structures hidden in data. A key technical challenge both in mode-seeking clustering and density ridge estimation is accurate estimation of the ratios of the first- and second-order density derivatives to the density. A naive approach takes a three-step approach of first estimating the data density, then computing its derivatives, and finally taking their ratios. However, this three-step approach can be unreliable because a good density estimator does not necessarily mean a good density derivative estimator, and division by the estimated density could significantly magnify the estimation error. To cope with these problems, we propose a novel estimator for the density-derivative-ratios. The proposed estimator does not involve density estimation, but rather directly approximates the ratios of density derivatives of any order. Moreover, we establish a convergence rate of the proposed estimator. Based on the proposed estimator, novel methods both for mode-seeking clustering and density ridge estimation are developed, and the respective convergence rates to the mode and ridge of the underlying density are also established. Finally, we experimentally demonstrate that the developed methods significantly outperform existing methods, particularly for relatively high-dimensional data.Peer reviewe

On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks

Likelihoods and posteriors of instrumental variable regression models with strongendogeneity and/or weak instruments may exhibit rather non-elliptical contours inthe parameter space. This may seriously affect inference based on Bayesian crediblesets. When approximating such contours using Monte Carlo integration methods likeimportance sampling or Markov chain Monte Carlo procedures the speed of the algorithmand the quality of the results greatly depend on the choice of the importance orcandidate density. Such a density has to be `close' to the target density in order toyield accurate results with numerically efficient sampling. For this purpose we introduce neural networks which seem to be natural importance or candidate densities, as they have a universal approximation property and are easy to sample from.A key step in the proposed class of methods is the construction of a neural network that approximates the target density accurately. The methods are tested on a set ofillustrative models. The results indicate the feasibility of the neural networkapproach.Markov chain Monte Carlo;Bayesian inference;credible sets;importance sampling;instrumental variables;neural networks;reduced rank

Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The use of the Hilbert–Huang transform in the analysis of biomedical signals has increased during the past few years, but its use for respiratory sound (RS) analysis is still limited. The technique includes two steps: empirical mode decomposition (EMD) and instantaneous frequency (IF) estimation. Although the mode mixing (MM) problem of EMD has been widely discussed, this technique continues to be used in many RS analysis algorithms. In this study, we analyzed the MM effect in RS signals recorded from 30 asthmatic patients, and studied the performance of ensemble EMD (EEMD) and noise-assisted multivariate EMD (NA-MEMD) as means for preventing this effect. We propose quantitative parameters for measuring the size, reduction of MM, and residual noise level of each method. These parameters showed that EEMD is a good solution for MM, thus outperforming NA-MEMD. After testing different IF estimators, we propose Kay¿s method to calculate an EEMD-Kay-based Hilbert spectrum that offers high energy concentrations and high time and high frequency resolutions. We also propose an algorithm for the automatic characterization of continuous adventitious sounds (CAS). The tests performed showed that the proposed EEMD-Kay-based Hilbert spectrum makes it possible to determine CAS more precisely than other conventional time-frequency techniques.Postprint (author's final draft

Remote sensing of floe size distribution and surface topography

Floe size can be measured by several properties p- for instance, area or mean caliper diameter. Two definitions of floe size distribution seem particularly useful. F(p), the fraction of area covered by floes no smaller than p; and N(p), the number of floes per unit area no smaller than p. Several summertime distributions measured are a graph, their slopes range from -1.7 to -2.5. The variance of an estimate is also calculated

Origin of lateral variation of seismic wave velocities and density in the deep mantle

Strong constraints can be placed on the origin of heterogeneity of seismic wave velocities and density if the observed ratios of various parameters are compared with mineral physics predictions. They include the shear to compressional wave velocity heterogeneity ratio, R ≡ δ log V / δ log V , the bulk sound to shear wave velocity heterogeneity ratio, R ≡ δ log V / δ log V , and the density to velocity heterogeneity ratio, R ≡ δ log ρ / δ log V . Using mineral physics considerations, we calculate these ratios in the lower mantle corresponding to the thermal and chemical origin of velocity and density heterogeneity. Both anharmonic and anelastic effects are considered for thermal origin. Anharmonic effects are estimated from the theoretical calculations as well as from laboratory measurements which show a marked increase in R with pressure from ∼1.5 to ∼2.1 in the lower mantle. Such a trend is marginally consistent with seismological observations showing an increase in R with depth (from ∼1.7 to ∼3.2 in the lower mantle). However, anharmonic effect alone cannot explain inferred low R (2.7) and corresponding negative values of R (and R ) in the deep lower mantle which cannot be accounted for by thermal or simple chemical heterogeneity such as the heterogeneity in the Fe/(Fe+Mg) and/or Mg/(Mg+Si) ratios. Possible causes of anomalies in this region are discussed, including the role of anisotropy and a combined effect of heterogeneity in Fe and Ca content. Copyright 2001 by the American Geophysical Union. s/p s p φ/s φ s ρ/s,p s,p s/p s/p ρ/s s/p s/p φ/s ρ/

Diapycnal advection by double diffusion and turbulence in the ocean

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 1999Observations of diapycnal mixing rates are examined and related to diapycnal advection for both double-diffusive and turbulent regimes. The role of double-diffusive mixing at the site of the North Atlantic Tracer Release Experiment is considered. The strength of salt-finger mixing is analyzed in terms of the stability parameters for shear and double-diffusive convection, and a nondimensional ratio of the thermal and energy dissipation rates. While the model for turbulence describes most dissipation occurring in high shear, dissipation in low shear is better described by the salt-finger model, and a method for estimating the salt-finger enhancement of the diapycnal haline diffusivity over the thermal diffusivity is proposed. Best agreement between tracer-inferred mixing rates and microstructure based estimates is achieved when the salt-finger enhancement of haline flux is taken into account. The role of turbulence occurring above rough bathymetry in the abyssal Brazil Basin is also considered. The mixing levels along sloping bathymetry exceed the levels observed on ridge crests and canyon floors. Additionally, mixing levels modulate in phase with the spring-neap tidal cycle. A model of the dissipation rate is derived and used to specify the turbulent mixing rate and constrain the diapycnal advection in an inverse model for the steady circulation. The inverse model solution reveals the presence of a secondary circulation with zonal character. These results suggest that mixing in abyssal canyons plays an important role in the mass budget of Antarctic Bottom Water.This work was supported by contracts N00014-92-1323 and N00014-97-10087 of the Office of Naval Research and grant OCE94-15589 of the National Science Foundation