Semi-Supervised Sound Source Localization Based on Manifold Regularization
نویسندگان
چکیده
منابع مشابه
Semi-Supervised Learning Based on Semiparametric Regularization
Semi-supervised learning plays an important role in the recent literature on machine learning and data mining and the developed semisupervised learning techniques have led to many data mining applications in recent years. This paper addresses the semi-supervised learning problem by developing a semiparametric regularization based approach, which attempts to discover the marginal distribution of...
متن کاملLinear Manifold Regularization for Large Scale Semi-supervised Learning
The enormous wealth of unlabeled data in many applications of machine learning is beginning to pose challenges to the designers of semi-supervised learning methods. We are interested in developing linear classification algorithms to efficiently learn from massive partially labeled datasets. In this paper, we propose Linear Laplacian Support Vector Machines and Linear Laplacian Regularized Least...
متن کاملSemi-supervised classification learning by discrimination-aware manifold regularization
Manifold regularization (MR) provides a powerful framework for semi-supervised classification (SSC) using both the labeled and unlabeled data. It first constructs a single Laplacian graph over the whole dataset for representing the manifold structure, and then enforces the smoothness constraint over such graph by a Laplacian regularizer in learning. However, the smoothness over such a single La...
متن کاملManifold regularization and semi-supervised learning: some theoretical analyses
Manifold regularization (Belkin et al., 2006) is a geometrically motivated framework for machine learning within which several semi-supervised algorithms have been constructed. Here we try to provide some theoretical understanding of this approach. Our main result is to expose the natural structure of a class of problems on which manifold regularization methods are helpful. We show that for suc...
متن کاملSemi-supervised Max-margin Topic Model with Manifold Posterior Regularization
Supervised topic models leverage label information to learn discriminative latent topic representations. As collecting a fully labeled dataset is often time-consuming, semi-supervised learning is of high interest. In this paper, we present an effective semi-supervised max-margin topic model by naturally introducing manifold posterior regularization to a regularized Bayesian topic model, named L...
متن کاملذخیره 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: IEEE/ACM Transactions on Audio, Speech, and Language Processing
سال: 2016
ISSN: 2329-9290,2329-9304
DOI: 10.1109/taslp.2016.2555085