43,708 research outputs found
Dimension Reduction by Mutual Information Discriminant Analysis
In the past few decades, researchers have proposed many discriminant analysis
(DA) algorithms for the study of high-dimensional data in a variety of
problems. Most DA algorithms for feature extraction are based on
transformations that simultaneously maximize the between-class scatter and
minimize the withinclass scatter matrices. This paper presents a novel DA
algorithm for feature extraction using mutual information (MI). However, it is
not always easy to obtain an accurate estimation for high-dimensional MI. In
this paper, we propose an efficient method for feature extraction that is based
on one-dimensional MI estimations. We will refer to this algorithm as mutual
information discriminant analysis (MIDA). The performance of this proposed
method was evaluated using UCI databases. The results indicate that MIDA
provides robust performance over different data sets with different
characteristics and that MIDA always performs better than, or at least
comparable to, the best performing algorithms.Comment: 13pages, 3 tables, International Journal of Artificial Intelligence &
Application
Nature-Inspired Learning Models
Intelligent learning mechanisms found in natural world are still unsurpassed in their learning performance and eficiency of dealing with uncertain information coming in a variety of forms, yet remain under continuous challenge
from human driven artificial intelligence methods. This work intends to demonstrate how the phenomena observed in physical world can be directly used to guide artificial learning models. An inspiration for the new
learning methods has been found in the mechanics of physical fields found in both micro and macro scale.
Exploiting the analogies between data and particles subjected to gravity, electrostatic and gas particle fields, new algorithms have been developed and applied to classification and clustering while the properties of the
field further reused in regression and visualisation of classification and classifier fusion. The paper covers extensive pictorial examples and visual interpretations of the presented techniques along with some testing over
the well-known real and artificial datasets, compared when possible to the traditional methods
A Family of Maximum Margin Criterion for Adaptive Learning
In recent years, pattern analysis plays an important role in data mining and
recognition, and many variants have been proposed to handle complicated
scenarios. In the literature, it has been quite familiar with high
dimensionality of data samples, but either such characteristics or large data
have become usual sense in real-world applications. In this work, an improved
maximum margin criterion (MMC) method is introduced firstly. With the new
definition of MMC, several variants of MMC, including random MMC, layered MMC,
2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the
MMC network is developed to learn deep features of images in light of simple
deep networks. Experimental results on a diversity of data sets demonstrate the
discriminant ability of proposed MMC methods are compenent to be adopted in
complicated application scenarios.Comment: 14 page
- …