Publications by Alexej's blog
Visualization of MRI data in R
Lately I was getting a little bored with genomic data (and then TCGA2STAT started to give me a segfault on my university’s high performance computing facility too :stuck_out_tongue:). So I decided to analyze some brain imaging data that I had lying around instead. The first step is to do some visual data exploration. In this blog post I present...
3067 sym R (933 sym/10 pcs) 16 img
Tired of doing real math 2 — grad school and coffee consumption
Lately I notice a sharp increase in my coffee consumption (reading Howard Schultz’s Starbucks book, which is actually quite good by the way, does not help either :grimacing:). Having recently transitioned into a new PhD program I started wondering whether my increased coffee consumption has something to do with my higher stress levels in the la...
3127 sym 6 img
Tired of doing real math 2 — grad school and coffee consumption
Lately I notice a sharp increase in my coffee consumption (reading Howard Schultz’s Starbucks book, which is actually quite good by the way, does not help either :grimacing:). Having recently transitioned into a new PhD program I started wondering whether my increased coffee consumption has something to do with my higher stress levels in the la...
3098 sym 6 img
2D contours of several penalty functions in statistics as GIF images
Many statistical modeling problems reduce to a minimization problem of the general form: or where $f$ is some type of loss function, $\mathbf{X}$ denotes the data, and $g$ is a penalty, also referred to by other names, such as “regularization term” (problems (1) and (2-3) are often equivalent by the way). Of course both, $f$ and $g$, may depe...
3907 sym 10 img
Contours of statistical penalty functions as GIF images
Many statistical modeling problems reduce to a minimization problem of the general form: or where is some type of loss function, denotes the data, and is a penalty, also referred to by other names, such as “regularization term” (problems (1) and (2-3) are often equivalent by the way). Of course both, and , may depend on further parameters...
4296 sym 133 img
Contours of statistical penalty functions as GIF images
Many statistical modeling problems reduce to a minimization problem of the general form: or where $f$ is some type of loss function, $\mathbf{X}$ denotes the data, and $g$ is a penalty, also referred to by other names, such as “regularization term” (problems (1) and (2-3) are often equivalent by the way). Of course both, $f$ and $g$, may depe...
4373 sym 14 img
Understanding the CANDECOMP/PARAFAC Tensor Decomposition, aka CP; with R code
A tensor is essentially a multi-dimensional array: a tensor of order one is a vector, which simply is a column of numbers, a tensor of order two is a matrix, which is basically numbers arranged in a rectangle, a tensor of order three looks like numbers arranged in rectangular box (or a cube, if all modes have the same dimension), an nth order (o...
6543 sym R (1248 sym/6 pcs) 55 img
Understanding the CANDECOMP/PARAFAC Tensor Decomposition, aka CP; with R code
A tensor is essentially a multi-dimensional array: a tensor of order one is a vector, which simply is a column of numbers, a tensor of order two is a matrix, which is basically numbers arranged in a rectangle, a tensor of order three looks like numbers arranged in rectangular box (or a cube, if all modes have the same dimension), an nth order (o...
6773 sym R (1248 sym/6 pcs) 12 img
Understanding the Tucker decomposition, and compressing tensor-valued data (with R code)
In many applications, data naturally form an n-way tensor with n > 2, rather than a “tidy” table. As mentioned in the beginning of my last blog post, a tensor is essentially a multi-dimensional array: a tensor of order one is a vector, which simply is a column of numbers, a tensor of order two is a matrix, which is basically numbers arranged...
3754 sym R (1095 sym/3 pcs) 58 img
Understanding the Tucker decomposition, and compressing tensor-valued data (with R code)
In many applications, data naturally form an n-way tensor with n > 2, rather than a “tidy” table. As mentioned in the beginning of my last blog post, a tensor is essentially a multi-dimensional array: a tensor of order one is a vector, which simply is a column of numbers, a tensor of order two is a matrix, which is basically numbers arranged...
8461 sym R (2535 sym/8 pcs) 4 img