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Dynamic Time Warping
In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed. For instance, similarities in walking could be detected using DTW, even if one person was walking faster than the other, or if there were accelerations and decelerations during the course of an observation. DTW has been applied to temporal sequences of video, audio, and graphics data — indeed, any data that can be turned into a one-dimensional sequence can be analyzed with DTW. A well-known application has been automatic speech recognition, to cope with different speaking speeds. Other applications include speaker recognition and online signature recognition. It can also be used in partial Shape analysis (digital geometry), shape matching applications. In general, DTW is a method that calculates an Optimal matching, optimal match between two given sequences (e.g. time series) with certain restriction and rules: * Every index from ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Needleman–Wunsch Algorithm
The Needleman–Wunsch algorithm is an algorithm used in bioinformatics to align protein or nucleotide sequences. It was one of the first applications of dynamic programming to compare biological sequences. The algorithm was developed by Saul B. Needleman and Christian D. Wunsch and published in 1970. The algorithm essentially divides a large problem (e.g. the full sequence) into a series of smaller problems, and it uses the solutions to the smaller problems to find an optimal solution to the larger problem. It is also sometimes referred to as the optimal matching algorithm and the global alignment technique. The Needleman–Wunsch algorithm is still widely used for optimal global alignment, particularly when the quality of the global alignment is of the utmost importance. The algorithm assigns a score to every possible alignment, and the purpose of the algorithm is to find all possible alignments having the highest score. Introduction This algorithm can be used for any two ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Time Warp Edit Distance
In the data analysis of time series, Time Warp Edit Distance (TWED) is a measure of similarity (or dissimilarity) between pairs of discrete time series, controlling the relative distortion of the time units of the two series using the physical notion of elasticity. In comparison to other distance measures, (e.g. DTW (dynamic time warping) or LCS (longest common subsequence problem)), TWED is a metric. Its computational time complexity is O(n^2), but can be drastically reduced in some specific situations by using a corridor to reduce the search space. Its memory space complexity can be reduced to O(n). It was first proposed in 2009 by P.-F. Marteau. Definition \delta_(A^p_1,B^q_1) = Min \begin \delta_(A^_1,B^q_1) + \Gamma(a^_p \to \Lambda) & \rm \\ \delta_(A^_1,B^_1) + \Gamma(a^_p \to b^_q) & \rm\\ \delta_(A^_1,B^_1) + \Gamma(\Lambda \to b^_q) & \rm \end whereas \Gamma(\alpha^_p \to \Lambda) = d_(a^_, a^_) + \nu \cdot (t_ - t_) + \lambda \Gamma(\alpha^ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mlpy
mlpy is a Python, open-source, machine learning library built on top of NumPy/SciPy, the GNU Scientific Library and it makes an extensive use of the Cython language. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3. Suited for general-purpose machine learning tasks, mlpy's motivating application field is bioinformatics, i.e. the analysis of high throughput omics data. Features * Regression: least squares, ridge regression, least angle regression, elastic net, kernel ridge regression, support vector machines (SVM), partial least squares (PLS) * Classification: linear discriminant analysis (LDA), Basic perceptron, Elastic Net, logistic regression, (Kernel) Support Vector Machines (SVM), Diagonal Linear Discri ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
