Consider a weighted or unweighted k-nearest neighbor graph that has been built on n data points drawn randomly according to some density p on R. We study the convergence of the shortest path distance in such graphs as the sample size tends to infinity. We prove that for unweighted kNN graphs, this distance converges to an unpleasant distance function on the underlying space whose properties are...

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Incomplete data is a common drawback that machine learning techniques need to deal with when solving real-life classification tasks. One of the most popular procedures for solving this kind of problems is the K-nearest neighbours (KNN) algorithm. In this paper, we present a weighted KNN approach using mutual information to impute and classify incomplete input data. Numerical results on both art...

In this paper, we proposed a weighted discrete K-nearest neighbor (weighted D-KNN) classification algorithm for detecting and evaluating emotion from Mandarin speech. In the experiments of the emotion recognition, Mandarin emotional speech database used contains five basic emotions, including anger, happiness, sadness, boredom and neutral, and the extracted acoustic features are Mel-Frequency C...

K nearest neighbor algorithm is one of the most frequently used techniques in data mining for its integrity and performance. Though the KNN algorithm is highly effective in many cases, it has some essential deficiencies, which affects the classification accuracy of the algorithm. First, the effectiveness of the algorithm is affected by redundant and irrelevant features. Furthermore, this algori...

A nonparametric statistical tool based on kernel function estimation is developed for spatial rainfall characterization. In this method, observations closer to the point of estimate are weighted higher using kernel function with a prescribed bandwidth. The kernel bandwidth is local and it extends only to the K Nearest Neighbor, KNN, observation. An optimal value for KNN is selected by cross val...

This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptivel...

In this paper, a novel prototype reduction algorithm is proposed, which aims at reducing the storage requirement and enhancing the online speed while retaining the same level of accuracy for a K-nearest neighbor (KNN) classifier. To achieve this goal, our proposed algorithm learns the weighted similarity function for a KNN classifier by maximizing the leave-one-out cross-validation accuracy. Un...