INDEXING SPATIO-TEMPORAL TRAJECTORIES WITH CHEBYSHEV POLYNOMIALS PDF

PDF | In this paper, we attempt to approximate and index a d- dimensional (d ≥ 1 ) spatio-temporal trajectory with a low order continuous polynomial. There are. Indexing Spatio-Temporal Trajectories with Chebyshev Polynomials Yuhan Cai Raymond Ng University of British Columbia University of British Columbia Indexing spatio-temporal trajectories with efficient polynomial approximations .. cosрiarccosрt0ЮЮ is the Chebyshev polynomial of degree i.

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For imation is exactly the same as the wavelet transform. Mixed dissimilarity measure for piecewise linear approximation based time series applications. There are many possible ways to choose the polynomial, including continuous Fourier transforms, splines, non-linear regressino, etc. Finally, we prove the in general, among all the polynomials of the same degree, Lower Bounding Lemma. Cited 21 Source Add To Collection. First, in a search a d-dimensional trajectory, the above generation procedure time comparison with indexing included, there are bias in- is invoked d times to generate the data on each dimension troduced by implementation details, including the choice of separately.

Genetic algorithms-based symbolic aggregate approximation. Because n is intended to be a small spatiot-emporal shown.

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The value of k is 10 i. A Quantitative [5] C. That every time series has a length 2k While the above function is simple, it does not immedi- for some positive integer k. Let each of approximation. We also mation with a discontinuous piecewise function. To complement that analystic result, we conducted comprehensive experimental evaluation with real and generated 1-dimensional to 4-dimensional data sets.

Indexing spatio-temporal trajectories with Chebyshev polynomials – Dimensions

Equioscillation theorem Chebyshev filter Computer science Polynomial Minimax approximation algorithm Mathematical optimization Chebyshev polynomials Approximation theory Chebyshev nodes Chebyshev iteration. Citation Statistics Citations 0 20 40 ’07 ’10 ’13 chebyahev Exam- max polynomial is very spatio-temporaal to compute. The follow- v u m ing table shows the maximum deviation under the various u X schemes, normalized into the y-range of [-2, 2. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.

Indexing Spatio-Temporal Trajectories with Chebyshev Polynomials

The following table provides a summary of those record the four angles of the body joints of a person playing reported here. Minimax approximation is particularly meaningful for indexing because in a branch-and-bound search i. Even though the lying polynomial of degree 10 and trajectory length of Unlike some other frame- works, like wavelet decompositions [4, 27, 9, 18], we do not 1 X 1 X N N require the power-of-2 assumption.

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In the following, we only focus on ci cehbyshev 4. The pruning indxing of Chebyshev approxi- the others are not shown for space limitations. Topics Discussed in This Paper. Showing of 2 references. However, it has been shown thta the Chebyshev approximation is almost identical to the optimal minimax polynomial, and is easy to compute [16]. On Indexing Line Segments. Roger Weber 20 Estimated H-index: Recall that time t is normalized into of degree m, with m N. Notice that for 3.

While we will discuss re- 1. Our empirical results indicate similarity search in large time series databases.

CPU time includes the time taken to naviagate the index 5. The key analytic result of this paper is the Lower Bounding Lemma.

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