A modified locality preserving partial least squares for classification problems

The problem of dimensionality reduction has recently receiveda lot of attention in areas such as machine learning, pattern recognition and computer vision. Dimensionality reduction is an important pre-processing step which aims to obtain a low-rank approximation problem with limited loss of vital information. It involves a mapping of high dimensional data samples into a low dimensional space such that the most important features in the original data are preserved. Locality preserving partial least squares (LPPLS) is a feature extraction method developed to preserve the local structure of data. The original LPPLS algorithm does not make use of class information of data when available. We propose in this paper a modification of LPPLS such that the class information of the data points istaken into consideration in the construction of the neighborhood graph. By doing so, our proposed method is allowed to have more discriminating power than LPPLS. Experimental results on a real-world dataset confirm the effectiveness of our proposed method.