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Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. . The purpose of data reduction can be two-fold: reduce the number of data records by eliminating invalid data or produce summary data and statistics at different aggregation levels for various applications
Vowpal wabbit has been used to learn a tera-feature (10 12) data-set on 1000 nodes in one hour. [1] Its scalability is aided by several factors: Out-of-core online learning: no need to load all data into memory; The hashing trick: feature identities are converted to a weight index via a hash (uses 32-bit MurmurHash3)
The process of feature selection aims to find a suitable subset of the input variables (features, or attributes) for the task at hand.The three strategies are: the filter strategy (e.g., information gain), the wrapper strategy (e.g., accuracy-guided search), and the embedded strategy (features are added or removed while building the model based on prediction errors).
In computing, data deduplication is a technique for eliminating duplicate copies of repeating data. Successful implementation of the technique can improve storage utilization, which may in turn lower capital expenditure by reducing the overall amount of storage media required to meet storage capacity needs.
Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. This process condenses extensive ...
While data reduction (compression, be it lossy or lossless) is a main goal of transform coding, it also allows other goals: one may represent data more accurately for the original amount of space [5] – for example, in principle, if one starts with an analog or high-resolution digital master, an MP3 file of a given size should provide a better ...
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In 2014, the National Digital Stewardship Alliance recommended developing file format action plans, stating "it is important to shift from more abstract considerations about file format obsolescence to develop actionable strategies for monitoring and mining information about the heterogeneous digital files the organizations are managing". [29]