Active learning, as a technique, aims to effectively label specific data points while operating within a designated query budget. Nevertheless, the majority of unsupervised active learning algorithms are based on shallow linear representation and lack sufficient interpretability. Furthermore, certain diversity-based methods face challenges in selecting samples that adequately represent the entire data distribution. Inspired by these reasons, in this paper, we propose an unsupervised active learning method on orthogonal projections to construct a deep neural network model. By optimizing the orthogonal projection process, we establish the connection between projection and active learning, consequently enhancing the interpretability of the proposed method. The proposed method can efficiently project the feature space onto a spanned subspace, deriving an indicator matrix while calculating the projection loss. Moreover, we consider the redundancy among samples to ensure both data point diversity and enhancement of clustering-based algorithms. Through extensive comparative experiments on six public datasets, the results demonstrate that the proposed method can effectively select more informative and representative samples and improve performance by up to 11%.

Distance metric learning aims to learn a positive semidefinite matrix such that similar samples are preserved with small distances while dissimilar ones are mapped with big values above a predefined margin. It can facilitate to improve the performance of certain learning tasks. In this article, distance metric learning and clustering are integrated into an unified framework via rank-reduced regression. First, distance metric learning is proved to be consistent with rank-reduced regression, which provides a new perspective to learn structured regularization matrices. Second, orthogonal and non-negative rank-reduced regression problems are addressed individually for clustering, and the corresponding algorithms with proved convergence are proposed. Finally, both distance metric learning and clustering are addressed simultaneously in the problem formulation, which may trigger some new insights for learning an effective clustering oriented low-dimensional embedding. To show the superior performance of the proposed method, we compare it with several state-of-the-art clustering approaches. And, extensive experiments on the test datasets demonstrate the superiority of the proposed method.

Understanding the characteristics of high-quality stereoscopic 3D (S3D) images has great significance for S3D image classification, quality assessment, and quality enhancement. Existing works assess the quality of an S3D image from a single perspective and use databases with subjective opinion scores obtained by conducting subjective experiments in labs. So the performance of these works in real applications is unclear and questionable. In this paper, we propose an S3D image quality assessment index based on two important factors, namely DisTortion and DisParity (DTDP). Distortion reflects the extent of distortion of each view of an S3D image, respectively. Disparity is a distinguishing factor for an S3D image as compared with a monocular image. We design some features to represent these two factors and use a random forest regression (RFR) to learn the mapping between the features and subjective opinion scores. The database we choose is the NVIDIA 3D VISION LIVE Highest Rated database which is from real application. The images and the subjective opinion scores are directly come from the website viewers all over the world. Experimental results demonstrate a superior performance of the proposed DTDP index as compared to the existing methods.