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Question

Existe uma trindade sagrada da segurança da informação. São três princípios ou propriedades, quais são elas?(novo com stilo small).

Answer

Confidencialidade, Integridade e Disponibilidade – conhecidos como CID. Se um ou mais desses princípios forem desrespeitados em algum momento, significa que houve um incidente de segurança da informação.

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Question

One category of statistical dimension reduction techniques is commonly called *** (PCA) or the *** (SVD).

Answer

One category of statistical dimension reduction techniques is commonly called principal components analysis (PCA) or the singular value decomposition (SVD).

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Question

One category of statistical dimension reduction techniques is commonly called principal components analysis (PCA) or the singular value decomposition (SVD). These techniques generally are applied in situations where the rows of a matrix represent *** of some sort and the columns of the matrix represent *** or *** (but this is by no means a ***).

Answer

One category of statistical dimension reduction techniques is commonly called principal components analysis (PCA) or the singular value decomposition (SVD). These techniques generally are applied in situations where the rows of a matrix represent observations of some sort and the columns of the matrix represent features or variables (but this is by no means a requirement).

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Question

One category of statistical dimension reduction techniques is commonly called principal components analysis (PCA) or the singular value decomposition (SVD). These techniques generally are applied in situations where the *** of a matrix represent observations of some sort and the *** of the matrix represent features or variables (but this is by no means a ***).

Answer

One category of statistical dimension reduction techniques is commonly called principal components analysis (PCA) or the singular value decomposition (SVD). These techniques generally are applied in situations where the rows of a matrix represent observations of some sort and the columns of the matrix represent features or variables (but this is by no means a requirement).

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Question

In an abstract sense, the SVD or PCA can be thought of as a way to approximate a ***-dimensional matrix (i.e. a high number of ***) with a a few ***-dimensional matrices.

Answer

In an abstract sense, the SVD or PCA can be thought of as a way to approximate a high-dimensional matrix (i.e. a large number of columns) with a a few low-dimensional matrices.

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Question

In an abstract sense, the SVD or PCA can be thought of as a way to approximate a high-dimensional *** with a a few low-dimensional ***.

Answer

In an abstract sense, the SVD or PCA can be thought of as a way to approximate a high-dimensional matrix with a a few low-dimensional matrices.

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Question

there’s a bit of data *** angle to SVD or PCA.

Answer

there’s a bit of data compression angle to SVD or PCA.

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Question

When confronted with matrix data a quick and easy thing to organize the data a bit is to apply an *** algorithm to it.

Answer

When confronted with matrix data a quick and easy thing to organize the data a bit is to apply an hierarchical clustering algorithm to it.

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Question

When confronted with matrix data a quick and easy thing to organize the data a bit is to apply an hierarchical clustering algorithm to it. Such a clustering can be visualized with the ***() function in R.

Answer

When confronted with matrix data a quick and easy thing to organize the data a bit is to apply an hierarchical clustering algorithm to it. Such a clustering can be visualized with the heatmap() function in R.

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Question

find the best matrix created with fewer variables (lower rank) that explains the original data.

this goal could be characterized as *** data compression.

Answer

find the best matrix created with fewer variables (lower rank) that explains the original data.

this goal could be characterized as lossy data compression.

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Question

find the best matrix created with fewer variables (lower rank) that explains the original data.

this goal could be characterized as lossy ***.

Answer

find the best matrix created with fewer variables (lower rank) that explains the original data.

this goal could be characterized as lossy data compression.

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Question

when comparing two matrices, the one with *** has lower rank.

Answer

when comparing two matrices, the one with less variables has lower rank.

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Question

when comparing two matrices, the one with less variables has lower ***.

Answer

when comparing two matrices, the one with less variables has lower rank.

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Question

If X is a matrix with each variable in a column and each observation in a row,

then the *** is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

Answer

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

If X is a matrix with each *** in a column and each *** in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

Answer

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

If X is a matrix with each variable in a *** and each observation in a ***,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

Answer

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

*** = ***

Answer

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of *** (left singular vectors) are orthogonal, the columns of *** (right singular vectors) are orthogonal and *** is a diagonal matrix of singular values.

Answer

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of U (left singular vectors) are orthogonal, the columns of $V$ (right singular vectors) are orthogonal and $D$ is a diagonal matrix of singular values.

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of U (left *** vectors) are orthogonal, the columns of $V$ (right *** vectors) are orthogonal and $D$ is a diagonal matrix of *** values.

Answer

[default - edit me]

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of U (left singular ***) are orthogonal, the columns of $V$ (right singular ***) are orthogonal and $D$ is a diagonal matrix of singular ***.

Answer

[default - edit me]

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of U (left singular vectors) are ***, the columns of $V$ (right singular vectors) are *** and $D$ is a *** of singular values.

Answer

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of U (left singular vectors) are orthogonal, the columns of $V$ (right singular vectors) are orthogonal and $D$ is a diagonal matrix of singular values.

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Principal components analysis is simply an application of the ***.

Answer

Principal components analysis is simply an application of the SVD.

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Question

The principal components are equal to the *** values if you first scale the data by subtracting the *** and dividing each *** by its ***.

Answer

The principal components are equal to the right singular values if you first scale the data by subtracting the column mean and dividing each column by its standard deviation.

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Question

The principal components are equal to the right singular values if you first *** the data by *** the column mean and *** each column by its standard deviation.

Answer

The principal components are equal to the right singular values if you first scale the data by subtracting the column mean and dividing each column by its standard deviation.

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Question

The principal components are equal to the right singular values if you first scale the data by subtracting the column mean and dividing each column by its standard deviation (that can be done with the ***() function in R).

Answer

The principal components are equal to the right singular values if you first scale the data by subtracting the column mean and dividing each column by its standard deviation (that can be done with the scale() function in R).

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Question

The SVD can be computed in R using the ***() function.

Answer

The SVD can be computed in R using the svd() function.

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Question

The svd() function in R returns a list containing three components named ***, ***, and ***.

Answer

The svd() function in R returns a list containing three components named u, d, and v.

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Question

The svd() function in R returns a list containing three components named u, d, and v.

The *** components correspond to the matrices of left and right singular vectors, respectively, while the *** component is a vector of singular values, corresponding to the diagonal of the matrix ***

Answer

The svd() function in R returns a list containing three components named u, d, and v.

The u and v components correspond to the matrices of left and right singular vectors, respectively, while the d component is a vector of singular values, corresponding to the diagonal of the matrix D

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scheduled repetition interval | last repetition or drill |

Question

The svd() function in R returns a list containing three components named u, d, and v.

The u and v components correspond to the matrices of ***, respectively, while the d component is a vector of ***, corresponding to the *** of the matrix D

Answer

The svd() function in R returns a list containing three components named u, d, and v.

The u and v components correspond to the matrices of left and right singular vectors, respectively, while the d component is a vector of singular values, corresponding to the diagonal of the matrix D

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Question

The singular values produced by the svd() in R are in order from *** to ***.

Answer

The singular values produced by the svd() in R are in order from largest to smallest.

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Question

The singular values produced by the svd() in R are in order from largest to smallest and when *** are proportional the amount of variance explained by a given singular vector.

Answer

The singular values produced by the svd() in R are in order from largest to smallest and when squared are proportional the amount of variance explained by a given singular vector.

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Question

The singular values produced by the svd() in R are in order from largest to smallest and when squared are proportional the amount of *** explained by a given ***.

Answer

The singular values produced by the svd() in R are in order from largest to smallest and when squared are proportional the amount of variance explained by a given singular vector.

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Question

PCA can be applied to the data by calling the ***() function in R.

Answer

PCA can be applied to the data by calling the prcomp() function in R.

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Question

Whether you call a procedure SVD or PCA really just depends on who you talk to.

*** and people with that kind of background will typically call it PCA while *** and *** will tend to call it SVD.

Answer

Whether you call a procedure SVD or PCA really just depends on who you talk to.

Statisticians and people with that kind of background will typically call it PCA while engineers and mathematicians will tend to call it SVD.

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Question

Whether you call a procedure SVD or PCA really just depends on who you talk to.

Statisticians and people with that kind of background will typically call it *** while engineers and mathematicians will tend to call it ***.

Answer

Whether you call a procedure SVD or PCA really just depends on who you talk to.

Statisticians and people with that kind of background will typically call it PCA while engineers and mathematicians will tend to call it SVD.

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Question

Most SVD and PCA routines simply cannot be applied if there are *** in the dataset.

Answer

Most SVD and PCA routines simply cannot be applied if there are missing values in the dataset.

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In the event of missing data, there are typically a series of questions that should be asked:

• Determine the reason for the missing data; what is the process that lead to the data being missing?

• Is the proportion of missing values so high as to invalidate any sort of analysis?

• Is there information in the dataset that would allow you to predict/infer the values of the missing data?

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Question

The impute package in R is available from the *** project.

Answer

The impute package in R is available from the Bioconductor project.

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