The significant economic contributions of the tourism industry in recent years impose an unprecedented force for data mining and machine learning methods to analyze tourism data. The intrinsic problems of raw data in tourism are largely related to the complexity, noise and nonlinearity in the data that may introduce many challenges for the existing data mining techniques such as rough sets and ...

In this letter, we show a direct relation between spectral embedding methods and kernel principal components analysis and how both are special cases of a more general learning problem: learning the principal eigenfunctions of an operator defined from a kernel and the unknown data-generating density. Whereas spectral embedding methods provided only coordinates for the training points, the analys...

An experiment was conducted comparing user performance on four data visualization techniques—an unstructured display condition consisting of a random one-dimensional (1D) list and three proximity-based representations including a 1D list ranked by a greedy nearest-neighbor algorithm and two 2D spatial visualizations using the ISOMAP layout algorithm and multidimensional scaling (MDS). Eighty-on...

Motor imagery EEG (MI-EEG), which reflects one’s active movement intention, has attracted increasing attention in rehabilitation therapy, and accurate and fast feature extraction is the key problem to successful applications. Based on wavelet packet decomposition (WPD) and SE-isomap, an adaptive feature extraction method is proposed in this paper. The MI-EEG is preprocessed to determine a more ...

This paper examines the efficacy of sampling-based low-rank approximation techniques when applied to large dense kernel matrices. We analyze two common approximate singular value decomposition techniques, namely the Nyström and Column sampling methods. We present a theoretical comparison between these two methods, provide novel insights regarding their suitability for various tasks and present ...

This paper we proposes an extended geodesic distance for head pose estimation. In ISOMAP, two approaches are applied for neighborhood construction, called k-neighbor and -neighbor. For the kneighbor, the number of the neighbors is a const k. For the other one, all the distances between the neighbors is less than . Either the k-neighbor or the -neighbor neglects the difference of each point. Thi...

Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have...

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...

Aims : We address the following questions: 1) Which are main vegetation types that currently occur in Arid Chaco? 2) Do those differ terms of floristic composition, endemism, chorotypes and life forms? 3) Is there any spatial association between environmental heterogeneity Study area The southwestern extreme Gran Chaco, Central-Western Argentina. Methods survey was based on a dataset comprising...

In this paper we propose a supervised version of the Isomap algorithm by incorporating class label information into a dissimilarity matrix in a financial analysis setting. On the credible assumption that corporates financial status lie on a low dimensional manifold, nonlinear dimensionality reduction based on manifold learning techniques has strong potential for bankruptcy analysis in financial...