This paper presents a constructive method for deriving an updated discriminant eigenspace for classification, when bursts of new classes of data is being added to an initial discriminant eigenspace in the form of random chunks. The proposed Chunk incremental linear discriminant analysis (I-LDA) can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract featu...

This paper proposes a combined canonical variate analysis (CVA) and Fisher discriminant analysis (FDA) scheme (denoted as CVA–FDA) for fault diagnosis, which employs CVA for pretreating the data and subsequently utilizes FDA for fault classification. In addition to the improved handling of serial correlations in the data, the utilization of CVA in the first step provides similar or reduced dime...

In the past several years, Linear Discriminant Analysis (LDA) is being replaced by Heteroscedastic Discriminant Analysis (HDA), to improve the performance of a recognition system that uses a mixture of diagonal covariance prototypes to model the data. A specific version HDA, popularly known as Maximum Likelihood Linear Transform (MLLT) is also used, on the features finally obtained. However the...

1 When patterns occur in the form of groups generated by the same source, distinctions between sources can be exploited to improve accuracy. We present a method for exploiting such ‘style’ consistency using a quadratic discriminant. We show that under reasonable assumptions on the feature and class distributions, the estimation of style parameters is simple and accurate.

Due to the gradual importance of e-Supply Chain Management (e-SCM) to the Smalland Medium-Sized Enterprises (SMEs) in Taiwan, this research intended to explore the critical factors which allowed the Smalland Medium-Sized Enterprises in Taiwan to employ e-SCM. The research further studied that under these critical factors, how the adoption of e-SCM affected their organizational achievements. In ...