Time-frequency analysis is performed for chaotic flow with a power spectrum estimator based on the phase-space neighborhood. The relation between the reference phase point and its nearest neighbors is demonstrated. The nearest neighbors, representing the state recurrences in the phase space reconstructed by time delay embedding, actually cover data segments with similar wave forms and thus poss...

In our previous comprehensive survey [41], we have categorized the disparate issues in distance metric learning. Within each of the four categories, we have summarized existing work, disclosed their essential connections, strengths and weaknesses. The first category is supervised distance metric learning, which contains supervised global distance metric learning, local adaptive supervised dista...

This paper presents new approach Hesitant Fuzzy K-nearest neighbour (HFK-nn) based document classification and numerical results analysis. The proposed classification Hesitant Fuzzy K-nearest neighbour (HFKnn) approach is based on hesitant Fuzzy distance. In this paper we have used hesitant Fuzzy distance calculations for document classification results. The following steps are used for classif...

The success of many machine learning algorithms (e.g. the nearest neighborhood classification and k-means clustering) depends on the representation of the data as elements in a metric space. Learning an appropriate distance metric from data is usually superior to the default Euclidean distance. In this paper, we revisit the original model proposed by Xing et al. [24] and propose a general formu...

In this paper, we present two novel class-based weighting methods for the Euclidean nearest neighbor algorithm and compare them with global weighting methods considering empirical results on a widely accepted time series classification benchmark dataset. Our methods provide higher accuracy than every global weighting in nearly half of the cases and they have better overall performance. We concl...

Distance metric learning is a successful way to enhance the performance of the nearest neighbor classifier. In most cases, however, the distribution of data does not obey a regular form and may change in different parts of the feature space. Regarding that, this paper proposes a novel local distance metric learning method, namely Local Mahalanobis Distance Learning (LMDL), in order to enhance t...

Euclidean distance, Hausdorff distance and SSP distance are discussed, and SSP distance is used to improve Isomap algorithm. Two methods are put forward for improving Isomap algorithm. One is aligning input data of original Isomap algorithm, the other is modifying Isomap algorithm itself. SSP distance is used to search neighbors and compose neighborhood graph, and the plot for each dimension of...