Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Independent component analysis: algorithms and applications
Neural Networks
Database retrieval for similar images using ICA and PCA bases
Engineering Applications of Artificial Intelligence
Collaborative filtering based on iterative principal component analysis
Expert Systems with Applications: An International Journal
Volatility modelling of multivariate financial time series by using ICA-GARCH models
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Multidimensional density shaping by sigmoids
IEEE Transactions on Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Analysis and modeling of multivariate chaotic time series based on neural network
Expert Systems with Applications: An International Journal
WSEAS Transactions on Information Science and Applications
MATH'08 Proceedings of the 13th WSEAS international conference on Applied mathematics
A source adaptive independent component analysis algorithm through solving the estimating equation
Expert Systems with Applications: An International Journal
Improved response modeling based on clustering, under-sampling, and ensemble
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
As the Internet spreads widely, it has become easier for companies to obtain and utilize valuable information on their customers. Nevertheless, many of them have difficulty in using the information effectively because of the huge amount of data from their customers that must to be analyzed. In addition, the data usually contains much noise due to anonymity of the Internet. Consequently, extracting the underlying meanings and canceling the noise of the collected customer data are crucial for the companies to implement their strategies for customer relationship management. As a novel solution, we propose the use of independent component analysis (ICA). ICA is a multivariate statistical tool which extracts independent components or sources of information, given only observed data that are assumed to be linear mixtures of some unknown sources. Moreover, ICA is able to reduce the dimension of the observed data, especially noisy variables. To validate the usefulness of ICA, we applied it to a real-world one-to-one marketing case. In this study, we used ICA as a preprocessing tool, and made a prediction for potential buyers using artificial neural networks (ANNs). We also applied PCA as a comparative model for ICA. The experimental results showed that ICA-preprocessed ANN outperformed all the comparative classifiers without preprocessing as well as PCA-preprocessed ANN.