Three Applications of Linear Dimension Reduction
Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data science and others. LDR can be employed when observations are recorded with many correlated features to reduce the number of features upon which statistical inference may be necessary. Some of the bene...
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| Abstract | Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data science and others. LDR can be employed when observations are recorded with many correlated features to reduce the number of features upon which statistical inference may be necessary. Some of the benets of LDR are to increase the signal to noise ratio in noisy data, rotate features into orthogonal space to reduce feature correlation eects, reduce the number of parameters to estimate, and decrease computational and memory costs associated with model tting. In this manuscript, we will discuss applications of LDR to poorly-posed classication, ill-posed classication, and statistical process monitoring. |
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| AbstractList | Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data science and others. LDR can be employed when observations are recorded with many correlated features to reduce the number of features upon which statistical inference may be necessary. Some of the benets of LDR are to increase the signal to noise ratio in noisy data, rotate features into orthogonal space to reduce feature correlation eects, reduce the number of parameters to estimate, and decrease computational and memory costs associated with model tting. In this manuscript, we will discuss applications of LDR to poorly-posed classication, ill-posed classication, and statistical process monitoring. |
| Author | Odom, Gabriel J |
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| Snippet | Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data science and others. LDR can be employed when observations... |
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| Title | Three Applications of Linear Dimension Reduction |
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