Quantum kernels hold great promise for offering computational advantages over classical learners, with the effectiveness of these kernels closely tied to the design of the feature map. However, the challenge of designing effective quantum feature maps for real-world datasets, particularly in the absence of sufficient prior information, remains a significant obstacle. In this study, we present a data-driven approach that automates the design of problem-specific quantum feature maps. Our approach leverages feature-selection techniques to handle high-dimensional data on near-term quantum machines with limited qubits, and incorporates a deep neural predictor to efficiently evaluate the performance of various candidate quantum kernels. Through extensive numerical simulations on different datasets, we demonstrate the superiority of our proposal over prior methods, especially for the capability of eliminating the kernel concentration issue and identifying the feature map with prediction advantages. Our work not only unlocks the potential of quantum kernels for enhancing real-world tasks, but also highlights the substantial role of deep learning in advancing quantum machine learning.

The spectral variability problem is a big challenge in hyperspectral unmixing. Endmember bundles have been used to address the spectral variability problem by adopting a bundle of endmember spectra to represent one kind of endmember class. Existing endmember bundle extraction algorithms mainly rely on the convex geometry assumption and integrate endmembers from image subsets as endmember bundles. On the one hand, they suffer from high risk of bad performance for real hyperspectral scene where the convex geometry assumption is not satisfied. On the other hand, endmember variabilities within image subsets are neglected, which may lose representative endmembers. In this paper, we propose a novel endmember bundle extraction framework to capture endmember variability by introducing a dynamic optimization mechanism. Endmember bundles are obtained by dynamically minimizing the root-mean-square error between original pixels and reconstructed pixels through an iteration process; and a particle swarm optimization method is introduced to find the optimal endmember combination in each iteration. The proposed endmember bundle extraction framework imposes no assumption on the hyperspectral data distribution and has great potential to be used in complex hyperspectral scenes. Experimental results on two real hyperspectral datasets demonstrate that the proposed algorithm is able to obtain endmember bundles that well express the spectral variability, and the performance of the proposed algorithm is competitive with the state-of-the-art algorithms.

Previous multi-view spectral clustering methods are a two-step strategy, which first learns a fixed common representation (or common affinity matrix) of all the views from original data and then conducts k-means clustering on the resulting common affinity matrix. The two-step strategy is not able to output reasonable clustering performance since the goal of the first step (i.e., the common affinity matrix learning) is not designed for achieving the optimal clustering result. Moreover, the two-step strategy learns the common affinity matrix from original data, which often contain noise and redundancy to influence the quality of the common affinity matrix. To address these issues, in this paper, we design a novel One-step Multi-view Spectral Clustering (OMSC) method to output the common affinity matrix as the final clustering result. In the proposed method, the goal of the common affinity matrix learning is designed to achieving optimal clustering result and the common affinity matrix is learned from low-dimensional data where the noise and redundancy of original high-dimensional data have been removed. We further propose an iterative optimization method to fast solve the proposed objective function. Experimental results on both synthetic datasets and public datasets validated the effectiveness of our proposed method, comparing to the state-of-the-art methods for multi-view clustering.

In this paper, we propose conducting Robust Graph Dimensionality Reduction (RGDR) by learning a transformation matrix to map original high-dimensional data into their low-dimensional intrinsic space without the influence of outliers. To do this, we propose simultaneously 1) adaptively learning three variables, \ie a reverse graph embedding of original data, a transformation matrix, and a graph matrix preserving the local similarity of original data in their low-dimensional intrinsic space; and 2) employing robust estimators to avoid outliers involving the processes of optimizing these three matrices. As a result, original data are cleaned by two strategies, \ie a prediction of original data based on three resulting variables and robust estimators, so that the transformation matrix can be learnt from accurately estimated intrinsic space with the helping of the reverse graph embedding and the graph matrix. Moreover, we propose a new optimization algorithm to the resulting objective function as well as theoretically prove the convergence of our optimization algorithm. Experimental results indicated that our proposed method outperformed all the comparison methods in terms of different classification tasks.

Data collection by social networking applications offers many opportunities for mining information, which provides a better understanding of social structures and their dynamic structures. Anonymization of social networks before they are published or shared is particularly important, since social network data usually contain much sensitive information on individuals. In this paper, we address the privacy problems of dynamic releases of social networks. We re-define the label-neighborhood attack model in dynamic social network releases. An adversary can use one-hop neighbor's network structure and label as background knowledge to identity the victim to learn more sensitive information. We propose a dynamic-l-diversity anonymized method to resist attacks. Experiments show that the proposed approach can retain much of the characteristics of the network while providing high utility.