The intrinsic dimensionality is, in essence, a local characteristic of the distribution, as shown in Fig. 6-4. If we establish small local regions around X1, X2, X3, etc., the dimensionality within the local region must be close to 1 [19],[20]. Because of this, the intrinsic dimensio...
We focus on the dimensionality reduction problem in multidimensional Hyperbolic spaces. A desirable property of our solution is its reconstructed boundedness. In other words, we can reconstruct the data from its dimension-reduced version to obtain an approximation of the original data, in which the ...
Hierarchical modeling of regional total water resources systems The importance of modeling for the planning and management of regional total water resources systems is discussed. Due to the complexity and the high dimensionality of the resulting regional planning and management problem, hierarchical ... ...
Reducing the dimensionality of a classification problem produces a more computationally-efficient system. Since the dimensionality of a classification prob... H Zeng,HJ Trussell - 《IEEE Transactions on Knowledge & Data Engineering》 被引量: 24发表: 2010年 Convolutional Neural Networks the development...
In modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak pe
A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases We address the problem of similarity search in large time series databases. We introduce a novel-dimensionality reduction technique that supports an indexi......
Multi-Objective Genetic Algorithms as an Effective Tool for Feature Selection in the Speech-Based Emotion Recognition Problem Feature selection is a quite important step in data analysis. Extracting relevant attributes may not only decrease the dimensionality of the dataset and, consequently, reduce time...
JiZhang, ...BingChen, inComputer Science Review, 2020 3.0.3Dimensionality reduction Dimensionality reductionis a necessary process in mostbig datarecognition frameworks that tackles the problem of learning and trainability of the model in the design. Essentially, dimensionality reduction is often seen ...
Dimensionality reduction means reducing the set’s dimension of your machine learning data. Learn all about it, the benefits and techniques now! Know more.
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