We propose to learn multiple local Mahalanobis distance metrics to perform knearest neighbor (kNN) classification of temporal sequences. Temporal sequences are first aligned by dynamic time warping (DTW); given the alignment path, similarity between two sequences is measured by the DTW distance, which is computed as the accumulated distance between matched temporal point pairs along the alignme...

We consider approximate nearest neighbor searching in metric spaces of constant doubling dimension. More formally, we are given a set S of n points and an error bound ε > 0. The objective is to build a data structure so that given any query point q in the space, it is possible to efficiently determine a point of S whose distance from q is within a factor of (1 + ε) of the distance between q and...

We revisit the task of learning a Euclidean metric from data. We approach this problem from first principles and formulate it as a surprisingly simple optimization problem. Indeed, our formulation even admits a closed form solution. This solution possesses several very attractive properties: (i) an innate geometric appeal through the Riemannian geometry of positive definite matrices; (ii) ease ...

In this paper, we provide an overview of fast nearest-neighbor search algorithms based on an &approximation}elimination' framework under a class of elimination rules, namely, partial distance elimination, hypercube elimination and absolute-error-inequality elimination derived from approximations of Euclidean distance. Previous algorithms based on these elimination rules are reviewed in the cont...

The purpose of this study was to predict whether mathematics students at Padang State University have the opportunity become campus activists using K-Nearest Neighbor Algorithm (KNN). This research will be used as a benchmark calculate how many can University. (KNN) is machine learning algorithm that has resistance training data where there lot noise and more effective for large data. itself di...

The k nearest neighbor (kNN) query on road networks finds the k closest points of interest (POIs) by network distance from a query point. A past study showed that a kNN technique using a simple Euclidean distance heuristic to generate candidate POIs significantly outperforms more complex techniques. While Euclidean distance is an effective lower bound when network distances represent physical d...

With the enormous growth of data, which exhibit sequentiality, it has become important to investigate the impact of embedded sequential information within the data. Sequential data are growing enormously, hence an efficient classification of sequential data is needed. k-Nearest Neighbor (kNN) has been used and proved to be an efficient classification technique for two-class problems. This paper...

Sequences of event intervals appear in several application domains including sign language, sensor networks, medicine, human motion databases, and linguistics. Such sequences comprise events that occur at time intervals and are time stamped at their start and end time. In this paper, we propose a new method, called IBSM, for comparing such sequences. IBSM performs full sequence matching using a...