Manifold learning with bi-stochastic kernels

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

Bi-stochastic kernels via asymmetric affinity functions

In this short letter we present the construction of a bi-stochastic kernel p for an arbitrary data set X that is derived from an asymmetric affinity function α. The affinity function α measures the similarity between points in X and some reference set Y. Unlike other methods that construct bi-stochastic kernels via some convergent iteration process or through solving an optimization problem, th...

متن کامل

Supervised Manifold Learning with Incremental Stochastic Embeddings

In this paper, we introduce an incremental dimensionality reduction approach for labeled data. The algorithm incrementally samples in latent space and chooses a solution that minimizes the nearest neighbor classification error taking into account label information. We introduce and compare two optimization approaches to generate supervised embeddings, i.e., an incremental solution construction ...

متن کامل

Manifold Stochastic Dynamics for Bayesian Learning

We propose a new Markov Chain Monte Carlo algorithm, which is a generalization of the stochastic dynamics method. The algorithm performs exploration of the state-space using its intrinsic geometric structure, which facilitates efficient sampling of complex distributions. Applied to Bayesian learning in neural networks, our algorithm was found to produce results comparable to the best state-of-t...

متن کامل

Manifold Identification for Regularized Stochastic Online Learning Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning

Iterative methods that calculate their steps from approximate subgradient directions have proved to be useful for stochastic learning problems over large and streaming data sets. When the objective consists of a loss function plus a nonsmooth regularization term whose purpose is to induce structure in the solution, the solution often lies on a low-dimensional manifold of parameter space along w...

متن کامل

Learning with Idealized Kernels

The kernel function plays a central role in kernel methods. Existing methods typically fix the functional form of the kernel in advance and then only adapt the associated kernel parameters based on empirical data. In this paper, we consider the problem of adapting the kernel so that it becomes more similar to the so-called ideal kernel. We formulate this as a distance metric learning problem th...

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: IMA Journal of Applied Mathematics

سال: 2019

ISSN: 0272-4960,1464-3634

DOI: 10.1093/imamat/hxy065