Locality Alignment Discriminant Analysis for Visualizing Regional English

In this paper, a novel dimensionality reduction algorithm named locality alignment discriminant analysis (LADA) for visualizing regional English is proposed. In the LADA algorithm, the proposed intrinsic graph or penalty graph measures the similarities between each pairwise textual slices, which can better characterize the intra-class compactness and inter-class separability; the projection matrix obtained by the proposed method is orthogonal, which can eliminate the redundancy between different projection directions, and is more effective for preserving the intrinsic geometry and improving the discriminating ability. To evaluate the performance of the algorithm, a regional written English corpus is designed and collected. Consequently, articles are split into slices and then transformed into 140-dimensional data points by 140 text style markers. Finally, variations existing in the regional written English are attempted to be recognized with our proposed LADA. The similarity among different types of English can be observed by the data plots. The results of visualization and numerical comparison indicate that LADA outperforms other existing algorithms in handling regional English data, as the proposed LADA can better preserve the local discriminative information embedded in the data, which is suitable for pattern classification.