Sparse Principal Component Analysis (sparse PCA) represents a significant advance in the field of dimensionality reduction for high-dimensional data. Unlike conventional Principal Component Analysis ...
PCA is an important tool for dimensionality reduction in data science and to compute grasp poses for robotic manipulation from point cloud data. PCA can also directly used within a larger machine ...
Sankhyā: The Indian Journal of Statistics, Series A (1961-2002), Vol. 26, No. 4 (Dec., 1964), pp. 329-358 (30 pages) The paper provides various interpretations of principal components in the analysis ...
A good way to see where this article is headed is to take a look at the screen shot of a demo program shown in Figure 1. The demo sets up a dummy dataset of six items: [ 5.1 3.5 1.4 0.2] [ 5.4 3.9 1.7 ...
Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy ...
The Heisenberg uncertainty principle, which has origins in physics, "states that there is a limit to the precision with which certain pairs of physical properties of a particle, such as position and ...
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