The K-nearest neighbour classifier is very effective and simple non-parametric technique in pattern classification; however, it only considers the distance closeness, but not the geometricalplacement of the k neighbors. Also, its classification performance is highly influenced by the neighborhood size k and existing outliers. In this paper, we propose a new local mean based k-harmonic nearest centroid neighbor (LMKHNCN) classifier in orderto consider both distance-based proximity, as well as spatial distribution of k neighbors. In our method, firstly the k nearest centroid neighbors in each class are found which are used to find k different local mean vectors, and then employed to compute their harmonic mean distance to the query sample. Lastly, the query sample is assigned to the class with minimum harmonic mean distance. The experimental results based on twenty-six real-world datasets shows that the proposed LMKHNCN classifier achieves lower error rates, particularly in small sample-size situations, and that it is less sensitive to parameter k when compared to therelated four KNN-based classifiers.

Smoothing images while preserving salient edges is a crucial task in computational photography. Existing edge-preserving filters suffer from various artifacts, such as halos, gradient reversals, and intensity shifts. Observing that various artifacts are strongly related to salient edges with large gradients, we propose a continuous mapping function to process the gradients. The proposed function is literally edge-preserving, i.e., it keeps large gradients intact while attenuating small gradients. We propose an L₁-regularized reconstruction model based on the processed gradients for edge-preserving image filtering. The L₁-regularization facilitates the edge-preserving property in the reconstructed results. To solve the proposed L₁-regularized model, we implement an efficient algorithm based on the alternating direction method of multipliers (ADMM) and Fourier domain optimization. We have conducted qualitative and quantitative experiments to evaluate the proposed filter. The results demonstrate that our filter better handles various artifacts and delivers superior image quality on various applications. The proposed filter is highly efficient, our GPU implementation takes 70ms to process a color image with 1 megapixel on an NVIDIA GTX 1070 GPU.

Multi-label classification remains a challenging task for high-dimensional data samples and their labels both increase the complexity of training models. In this paper, we propose a Robust Label and Feature Space Co-Learning method, referred to as RLFSCL, for multi-label classification. Different from traditional multi-label classification methods which focus on feature space learning through regression directly between data samples and labels, our proposed method can further learn robust low rank label space from this traditional regression method. Therefore, our RLFSCL can learn better low rank feature and label representations simultaneously in original noisy and high dimensional spaces. Experimental comparison on five benchmark datasets, including Rcv1s5, Cal500, and Corel16k4 shows that the proposed RLFSCL algorithm outperforms state-of-the-art multi-label classification methods. The code of RLFSCL is made available on https://github.com/JingChuanTang/RLFSCL.

In the last years, subspace‐based multi‐view face recognition has attracted increasing attention and many related methods have been proposed. However, the most existing methods ignore the specific local structure of different views. This drawback can cause these methods' discriminating ability to degrade when many noisy samples exist in data. To tackle this problem, a multi‐view low‐rank representation method is proposed, which exploits both intrinsic relationships and specific local structures of different views simultaneously. It is achieved by hierarchical Bayesian methods that constrain the low‐rank representation of each view so that it matches a linear combination of an intrinsic representation matrix and a specific representation matrix to obtain common and specific characteristics of different views. The intrinsic representation matrix holds the consensus information between views, and the specific representation matrices indicate the diversity among views. Furthermore, the model injects a clustering structure into the low‐rank representation. This approach allows for adaptive adjustment of the clustering structure while pursuing the optimization of the low‐rank representation. Hence, the model can well capture both the relationship between data and the clustering structure explicitly. Extensive experiments on several datasets demonstrated the effectiveness of the proposed method compared to similar state‐of‐the‐art methods in classification and clustering.