Sidiropoulos, N.D., Bro, R. "Mathematical programming algorithms for regression-based nonlinear filtering in R  Home/Search Document Details and Download
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Fast Time Sequence Indexing for Arbitrary L p Norms - Yi, Faloutsos (2000) (35 citations) (Correct)
....when p = 1, it is called the maximum norm and can be reformulated 5 as follows: L1 ( x Gamma y) L max i=1 jx i Gamma y i j It is known that L 2 norm is optimal (in the Maximum Likelihood sense) when measurement errors are additive, i.i.d. independent, identically distributed) Gaussian [31]. It has been the most popular dissimilarity measure in similar time sequence matching [5, 14, 16, 28] Linear correlation coefficient was used in [21] but it can be effectively converted to L 2 norm without loss of information [13] L 1 norm is optimal when measurement errors are additive, ....
....[5, 14, 16, 28] Linear correlation coefficient was used in [21] but it can be effectively converted to L 2 norm without loss of information [13] L 1 norm is optimal when measurement errors are additive, i.i.d. Laplacian (or Double Exponential) hence more robust against impulsive noise [31]. L 1 has been used in the context of robust (parametric or non parametric) regression [20, 29, 31] for many applications including time sequences [20, 10] More recently, it was also used in [15] for their hashing based similarity search technique. L1 was used for atomic matching in a more ....
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N. D. Sidiropoulos and R. Bros. Mathematical Programming Algorithms for Regression-based Non-linear Filtering in R N . IEEE Trans. on Signal Processing, Mar 1999.
Time-Varying Fair Queueing Scheduling for Multicode .. - Stamoulis.. (2004) Self-citation (Sidiropoulos) (Correct)
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N. D. Sidiropoulos and R. Bro, "Mathematical programming algorithms for regression-based nonlinear filtering in IR ," IEEE Trans. Signal Processing, vol. 47, pp. 771--782, Mar. 1999.
Time-Varying Fair Queuing Scheduling For Multicode.. - Stamoulis.. Self-citation (Sidiropoulos) (Correct)
.... . With non trivial QoS constraints, the execution time of the algorithm is decreased as transitions in the trellis diagram are expurgated. However, when is relatively large with respect to J , for all , then there is an alternative solution. Following the material in [17], we can do DP over breakpoint variables d : we start by writing the cost as ( 3 is the point that the DP trellis switches from allocation to allocation 1; is the point where the DP trellis switches from allocation 1 to allocation 2, and so ....
N.D. Sidiropoulos and R. Bro, "Mathematical programming algorithms for regression-based nonlinear filtering in IR & ," IEEE Transactions on Signal Processing, vol. 47, no. 3, pp. 771--782, March 1999.
Monotonic Regression Filters for Trending Deterioration.. - Dimitry Gorinevsky American (Correct)
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Sidiropoulos, N.D., Bro, R. "Mathematical programming algorithms for regression-based nonlinear filtering in R
Moving Horizon Filter for Monotonic Trends - Sikandar Samar Stanford (Correct)
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Sidiropoulos, N.D., Bro, R. "Mathematical programming algorithms for regression-based nonlinear filtering in R
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