Semi-Supervised Dimensionality Reduction by Linear Compression and Stretching

نویسندگان
چکیده

برای دانلود رایگان 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

Semi-Supervised Dimensionality Reduction

Dimensionality reduction is among the keys in mining highdimensional data. This paper studies semi-supervised dimensionality reduction. In this setting, besides abundant unlabeled examples, domain knowledge in the form of pairwise constraints are available, which specifies whether a pair of instances belong to the same class (must-link constraints) or different classes (cannot-link constraints)...

متن کامل

Semi-supervised learning in Spectral Dimensionality Reduction

Biometric face data are essentially high dimensional data and as such are susceptible to the well-known problem of the curse of dimensionality when analyzed using machine learning techniques. Various dimensionality reduction methods have been proposed in the literature to represent high dimensional data in a lower dimensional space. Research has shown that biometric face data are non-linear in ...

متن کامل

Multiple view semi-supervised dimensionality reduction

Article history: Received 21 September 2008 Received in revised form 14 July 2009 Accepted 24 July 2009

متن کامل

Spectral Methods for Linear and Non-Linear Semi-Supervised Dimensionality Reduction

We present a general framework of spectral methods for semi-supervised dimensionality reduction. Applying an approach called manifold regularization, our framework naturally generalizes existent supervised frameworks. Furthermore, by our two semi-supervised versions of the representer theorem, our framework can be kernelized as well. Using our framework, we give three examples of semi-supervise...

متن کامل

Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction

Semi-supervised regression based on the graph Laplacian suffers from the fact that the solution is biased towards a constant and the lack of extrapolating power. Based on these observations, we propose to use the second-order Hessian energy for semi-supervised regression which overcomes both these problems. If the data lies on or close to a low-dimensional submanifold in feature space, the Hess...

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: IEEE Access

سال: 2020

ISSN: 2169-3536

DOI: 10.1109/access.2020.2971562