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Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary.
Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text, where each line of the file typically represents one data record.
Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions and assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. [70] A popular heuristic method for sparse dictionary learning is the k-SVD algorithm. Sparse ...
The QUARTILE function is a legacy function from Excel 2007 or earlier, giving the same output of the function QUARTILE.INC. In the function, array is the dataset of numbers that is being analyzed and quart is any of the following 5 values depending on which quartile is being calculated. [8]
The Kruskal-Wallis test can be implemented in many programming tools and languages. We list here only the open source free software packages: In Python's SciPy package, the function scipy.stats.kruskal can return the test result and p-value. [18] R base-package has an implement of this test using kruskal.test. [19]
Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. [1] Application domains include cluster analysis in computer vision and image processing .
This method of Dictionary-Based Machine translation explores a different paradigm from systems such as LMT. An example-based machine translation system is supplied with only a "sentence-aligned bilingual corpus". [3] Using this data the translating program generates a "word-for-word bilingual dictionary" [3] which is used for further translation.
where n is the number of keys, m is the number of buckets, and b j is the number of items in bucket j. A ratio within one confidence interval (such as 0.95 to 1.05) is indicative that the hash function evaluated has an expected uniform distribution.