The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-based collaborative filtering algorithms, such as knearest neighbor, have been shown to be quite vulnerable to such attacks. In this paper, we examine the robustness of model-based recommendation algorithms in the face...

This paper sheds light on some fundamental connections of the diffusion decision making model of neuroscience and cognitive psychology with k-nearest neighbor classification. We show that conventional k-nearest neighbor classification can be viewed as a special problem of the diffusion decision model in the asymptotic situation. By applying the optimal strategy associated with the diffusion dec...

The first step in graph-based semi-supervised classification is to construct a graph from input data. While the k-nearest neighbor graphs have been the de facto standard method of graph construction, this paper advocates using the less well-known mutual k-nearest neighbor graphs for high-dimensional natural language data. To compare the performance of these two graph construction methods, we ru...

Large margin nearest neighbor classification (LMNN) is a popular technique to learn a metric that improves the accuracy of a simple knearest neighbor classifier via a convex optimization scheme. However, the optimization problem is convex only under the assumption that the nearest neighbors within classes remain constant. In this contribution we show that an iterated LMNN scheme (multi-pass LMN...

Abstract Motivated by a broad range of potential applications in topological and geometric inference, we introduce a weighted version of the knearest neighbor density estimate. Various pointwise consistency results of this estimate are established. We present a general central limit theorem under the lightest possible conditions. In addition, a strong approximation result is obtained and the ch...

Malware is security threat that can break computer operation without knowing user’s information and it is difficult to identify its behavior. We can use signature based matching technique, encryption and decryption engines, metamorphism based method and KNN (Knearest neighbor) algorithm to identify the behavior of malware. Among all these techniques a pattern based technique is well famous for ...

Nearest neighbor search and k-nearest neighbor graph construction are two fundamental issues arise from many disciplines such as information retrieval, data-mining, machine learning and computer vision. Despite continuous efforts have been taken in the last several decades, these two issues remain challenging. They become more and more imminent given the big data emerges in various fields and h...

A k-nearest neighbor search algorithm for the Pyramid technique is presented. The Pyramid technique divides d-dimensional data space into 2d pyramids. Given a query point q, we initialize the radius of a range query to be the furthest distance of the k candidate nearest neighbors from q in the pyramid which q is in, then examine the rest of the pyramids one by one. After one pyramid is checked,...

Abstratct: In this paper, we present a method for classification of medical images. Wavelet features of different modalities of medical images are extracted. Then mean and standard deviation of extracted wavelet features are computed. We utilize KNearest Neighbor classifier to classify medical imaging modalities as X-ray, MRI and CT. Experiments are conducted on medical database containing 4,50...