# Adaptive Locality Preserving Regression

## TL;DR

This paper introduces ALPR, a flexible discriminative regression method that preserves data structure, performs feature selection, and enhances interpretability for improved classification accuracy.

## Contribution

It proposes a novel adaptive target learning and locality preserving constraint with l21 norm regularization for feature selection and interpretability.

## Key findings

- Effective in synthetic and real-world datasets
- Preserves data structure and enhances discriminative power
- Selects important features and reduces noise

## Abstract

This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional `Frobenius norm' with the special l21 norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owing to the row-sparsity property of the l21 norm. Extensive experiments conducted on the synthetic database with manifold structure and many real-world databases prove the effectiveness of the proposed method.