###### Abstract  
In the era of big data, feature engineering has proved its efficiency and importance in dimensionality reduction and useful information extraction from original features. Feature engineering can be expressed as dimensionality reduction and is divided into two types of methods, namely, feature selection and feature extraction. Each method has its pros and cons. There are a lot of studies that combine these methods. The sparse autoencoder (SAE) is a representative deep feature learning method that combines feature selection with feature extraction. However, existing SAEs do not consider feature importance during training. It causes extracting irrelevant information. In this paper, we propose an interactive guiding sparse autoencoder (IGSAE) to guide the information by two interactive guiding layers and sparsity constraints. The interactive guiding layers keep the main distribution using Wasserstein distance, which is a metric of distribution difference, and it suppresses the leverage of guiding features to prevent overfitting. We perform our experiments using four datasets that have different dimensionalities and numbers of samples. The proposed IGSAE method produces a better classification performance compared to other dimensionality reduction methods. dimensionality reduction; autoencoder; feature extraction; feature selection; guiding layer; regularization +
Footnote †: journal: Medical  
information gain [17], chi-square test [18], etc. However, these methods have the disadvantage of removing potential non-noise factors of the original data. On the other hand, feature extraction methods are usually based on the reconstruction of features and projection from high-dimensional data into low-dimensional subspace. This kind of algorithm normally preserves the variance of features, so it has high information retention. Since these algorithms consider almost all features in the transformation process, the dimensionality reduction effect is usually depleted. Principal component analysis (PCA) [19; 20], canonical correlation analysis (CCA) [21], linear discriminant analysis (LDA) [22; 23], and autoencoder using machine learning models for feature extraction are representative methods. However, an autoencoder has overfitting problems and highly complex structural issues. Improved structures have been proposed in recent years to solve these problems. [PERSON] et al. [24] applied a stacked autoencoder (SAE) to hyperspectral image classification to generate high-quality hyperspectral image data to maximize the performance of the task. [PERSON] et al. [25] applied a stacked autoencoder to fault diagnosis of rolling bearings and obtained promising results. They increase the size of the network to obtain a better result. However, the autoencoder-based simple method is still a field of feature extraction, so it is difficult to modify and remove features with less relevance, which is the advantage of feature selection. To improve this shortcoming, new techniques applying regularization have been studied. Notably, the stacked sparse autoencoder [26; 27; 28] is one of the representative deep autoencoder methods that have feature selection ideas. The stacked sparse autoencoder (SSAE) improved the autoencoder by a combination of two advanced autoencoders: a sparse autoencoder and a stacked autoencoder. A sparse autoencoder adds sparse constraints to the autoencoder. It restricts the useless node to reduce the dimension efficiently. A stacked autoencoder improves the training performance by stacking autoencoders. It mediates to deeply learn the features in comparison with the simple learning process of traditional autoencoders. [PERSON] et al. [29] proposed the structure of a group sparse autoencoder (GAE) with regularization applied to solve the overfitting, which is a chronic problem of autoencoders. SSAE's regularization-based methods achieved successful results in various applications. However, it is difficult to apply the dynamic and efficient feature selection method due to its structural issues.