A Wasserstein-Type Distance in the Space of Gaussian Mixture Models

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Sliced Wasserstein Distance for Learning Gaussian Mixture Models

Gaussian mixture models (GMM) are powerful parametric tools with many applications in machine learning and computer vision. Expectation maximization (EM) is the most popular algorithm for estimating the GMM parameters. However, EM guarantees only convergence to a stationary point of the log-likelihood function, which could be arbitrarily worse than the optimal solution. Inspired by the relation...

متن کامل

Evaluation of Distance Measures Between Gaussian Mixture Models of MFCCs

In music similarity and in the related task of genre classification, a distance measure between Gaussian mixture models is frequently needed. We present a comparison of the KullbackLeibler distance, the earth movers distance and the normalized L2 distance for this application. Although the normalized L2 distance was slightly inferior to the Kullback-Leibler distance with respect to classificati...

متن کامل

Deriving Cluster Analytic Distance Functions from Gaussian Mixture Models

The reliable detection of clusters in datasets of non-trivial dimensionality is notoriously difficult. Clustering algorithms are generally driven by some distance function (usually Euclidean) defined over pairs of examples, which implicitly treats distances within and between clusters alike. In this paper, a more effective distance measure is proposed, derived from an a priori estimated Gaussia...

متن کامل

On Wasserstein Geometry of the Space of Gaussian Measures

Abstract. The space which consists of measures having finite second moment is an infinite dimensional metric space endowed with Wasserstein distance, while the space of Gaussian measures on Euclidean space is parameterized by mean and covariance matrices, hence a finite dimensional manifold. By restricting to the space of Gaussian measures inside the space of probability measures, we manage to ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: SIAM Journal on Imaging Sciences

سال: 2020

ISSN: 1936-4954

DOI: 10.1137/19m1301047