DeepAI AI Chat
Log In Sign Up

Deep Embedding Kernel

04/16/2018
by   Linh Le, et al.
0

In this paper, we propose a novel supervised learning method that is called Deep Embedding Kernel (DEK). DEK combines the advantages of deep learning and kernel methods in a unified framework. More specifically, DEK is a learnable kernel represented by a newly designed deep architecture. Compared with pre-defined kernels, this kernel can be explicitly trained to map data to an optimized high-level feature space where data may have favorable features toward the application. Compared with typical deep learning using SoftMax or logistic regression as the top layer, DEK is expected to be more generalizable to new data. Experimental results show that DEK has superior performance than typical machine learning methods in identity detection, classification, regression, dimension reduction, and transfer learning.

READ FULL TEXT
10/07/2019

Deep Kernel Learning via Random Fourier Features

Kernel learning methods are among the most effective learning methods an...
07/04/2019

Fair Kernel Regression via Fair Feature Embedding in Kernel Space

In recent years, there have been significant efforts on mitigating uneth...
09/02/2011

Gradient-based kernel dimension reduction for supervised learning

This paper proposes a novel kernel approach to linear dimension reductio...
07/06/2017

Indefinite Kernel Logistic Regression

Traditionally, kernel learning methods requires positive definitiveness ...
11/14/2016

Post Training in Deep Learning with Last Kernel

One of the main challenges of deep learning methods is the choice of an ...
11/26/2021

Unsupervised MKL in Multi-layer Kernel Machines

Kernel based Deep Learning using multi-layer kernel machines(MKMs) was p...