Dual Low-Rank Decompositions for Robust Cross-View Learning

A novel multi-feature learning model for disease diagnosis using face skin images

BACKGROUND

Facial skin characteristics can provide valuable information about a patient's underlying health conditions.

OBJECTIVE

In practice, there are often samples with divergent characteristics (commonly known as divergent samples) that can be attributed to environmental factors, living conditions, or genetic elements. These divergent samples significantly degrade the accuracy of diagnoses.

METHODOLOGY

To tackle this problem, we propose a novel multi-feature learning method called Multi-Feature Learning with Centroid Matrix (MFLCM), which aims to mitigate the influence of divergent samples on the accurate classification of samples located on the boundary. In this approach, we introduce a novel discriminator that incorporates a centroid matrix strategy and simultaneously adapt it to a classifier in a unified model. We effectively apply the centroid matrix to the embedding feature spaces, which are transformed from the multi-feature observation space, by calculating a relaxed Hamming distance. The purpose of the centroid vectors for each category is to act as anchors, ensuring that samples from the same class are positioned close to their corresponding centroid vector while being pushed further away from the remaining centroids.

RESULTS

Validation of the proposed method with clinical facial skin dataset showed that the proposed method achieved F1 scores of 92.59%, 83.35%, 82.84% and 85.46%, respectively for the detection the Healthy, Diabetes Mellitus (DM), Fatty Liver (FL) and Chronic Renal Failure (CRF).

CONCLUSION

Experimental results demonstrate the superiority of the proposed method compared with typical classifiers single-view-based and state-of-the-art multi-feature approaches. To the best of our knowledge, this study represents the first to demonstrate concept of multi-feature learning using only facial skin images as an effective non-invasive approach for simultaneously identifying DM, FL and CRF in Han Chinese, the largest ethnic group in the world.

Harmonization Shared Autoencoder Gaussian Process Latent Variable Model With Relaxed Hamming Distance

Multiview learning has shown its superiority in visual classification compared with the single-view-based methods. Especially, due to the powerful representation capacity, the Gaussian process latent variable model (GPLVM)-based multiview approaches have achieved outstanding performances. However, most of them only follow the assumption that the shared latent variables can be generated from or projected to the multiple observations but fail to exploit the harmonization in the back constraint and adaptively learn a classifier according to these learned variables, which would result in performance degradation. To tackle these two issues, in this article, we propose a novel harmonization shared autoencoder GPLVM with a relaxed Hamming distance (HSAGP-RHD). Particularly, an autoencoder structure with the Gaussian process (GP) prior is first constructed to learn the shared latent variable for multiple views. To enforce the agreement among various views in the encoder, a harmonization constraint is embedded into the model by making consistency for the view-specific similarity. Furthermore, we also propose a novel discriminative prior, which is directly imposed on the latent variable to simultaneously learn the fused features and adaptive classifier in a unit model. In detail, the centroid matrix corresponding to the centroids of different categories is first obtained. A relaxed Hamming distance (RHD)-based measurement is subsequently presented to measure the similarity and dissimilarity between the latent variable and centroids, not only allowing us to get the closed-form solutions but also encouraging the points belonging to the same class to be close, while those belonging to different classes to be far. Due to this novel prior, the category of the out-of-sample is also allowed to be simply assigned in the testing phase. Experimental results conducted on three real-world data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art approaches.

Low Rank Regularization: A review

Low Rank Regularization (LRR), in essence, involves introducing a low rank or approximately low rank assumption to target we aim to learn, which has achieved great success in many data analysis tasks. Over the last decade, much progress has been made in theories and applications. Nevertheless, the intersection between these two lines is rare. In order to construct a bridge between practical applications and theoretical studies, in this paper we provide a comprehensive survey for LRR. Specifically, we first review the recent advances in two issues that all LRR models are faced with: (1) rank-norm relaxation, which seeks to find a relaxation to replace the rank minimization problem; (2) model optimization, which seeks to use an efficient optimization algorithm to solve the relaxed LRR models. For the first issue, we provide a detailed summarization for various relaxation functions and conclude that the non-convex relaxations can alleviate the punishment bias problem compared with the convex relaxations. For the second issue, we summarize the representative optimization algorithms used in previous studies, and analyze their advantages and disadvantages. As the main goal of this paper is to promote the application of non-convex relaxations, we conduct extensive experiments to compare different relaxation functions. The experimental results demonstrate that the non-convex relaxations generally provide a large advantage over the convex relaxations. Such a result is inspiring for further improving the performance of existing LRR models.