Efficient Smooth GMM and Dimension Reduction
نویسندگان
چکیده
We propose a new GMM criterion for models defined by conditional moment restrictions based on local averaging. It resembles a statistic based on smoothing techniques used in specification testing. Depending on whether the smoothing parameter is fixed or decreases to zero with the sample size, our approach defines a whole class of estimators. We show that consistency and asymptotic normality follows in both cases. However we show that at a first-order, letting the smoothing parameter tend to zero yields a semiparametric efficient estimator, and we provide a two-step efficient version. We also investigate a dimension-reduction device in the context of smooth GMM. While the resulting estimator does not attain the semiparametric efficiency bound, its higher-order properties may be preferable.
منابع مشابه
Local fuzzy PCA based GMM with dimension reduction on speaker identification
To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with fuzzy clustering. The proposed method firstly partitions the data space into several disjoint clusters by fuzzy clustering, and then performs PCA using the fuzzy covariance matrix on each cluster. Finally, the GMM for speaker is obtained from ...
متن کاملCharting a Manifold
We construct a nonlinear mapping from a high-dimensional sample space to a low-dimensional vector space, effectively recovering a Cartesian coordinate system for the manifold from which the data is sampled. The mapping preserves local geometric relations in the manifold and is pseudo-invertible. We show how to estimate the intrinsic dimensionality of the manifold from samples, decompose the sam...
متن کاملOutlier Detection In Large-scale Traffic Data By Naïve Bayes Method and Gaussian Mixture Model Method
It is meaningful to detect outliers in traffic data for traffic management. However, this is a massive task for people from large-scale database to distinguish outliers. In this paper, we present two methods: Kernel Smoothing Näıve Bayes (NB) method and Gaussian Mixture Model (GMM) method to automatically detect any hardware errors as well as abnormal traffic events in traffic data collected at...
متن کاملDimension reduction approaches for SVM based speaker age estimation
This paper presents two novel dimension reduction approaches applied on the gaussian mixture model (GMM) supervectors to improve age estimation speed and accuracy. The GMM supervector embodies many speech characteristics irrelevant to age estimation and like noise, they are harmful to the system’s generalization ability. In addition, the support vectors machine (SVM) testing computation grows w...
متن کاملGmm Based on Local Robust Pca for Speaker Identification
ABSTRACT: To solve the problems of outliers and high dimensionality of training feature vectors in speaker identification, in this paper, we propose an efficient GMM based on local robust PCA with VQ. The proposed method firstly partitions the data space into several disjoint regions by VQ, and then performs robust PCA using the iteratively reweighted covariance matrix in each region. Finally, ...
متن کامل