It says we considered the lengths zero-meaned accelerometer vectors & created a feature for the mean and standard deviation of this value. and I vì not underst& what is it zero-meaned vectors?
Can any toàn thân help me?
I found only this information https://www.quora.com/What-does-it-mean-when-a-vector-is-zero-mean but I"m not sure about it.
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asked Sep 25 "16 at 13:59
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"Zero-meaned" means the vector has been transformed so that its mean is 0.
Typically, you would vày this by subtracting the mean of each column from that column. (This is for dimensional as well as algorithmic reasons; you don"t want to subtract a person"s weight from their height.)
It sounds lượt thích here they"re actually talking about the row mean--that is, $(-0.6946377, 12.680544, 0.50395286)$ would be transformed khổng lồ $(-4.857924, 8.5172577, -3.65933344, 4.1632863, 7.40047)$, where the first three are the original features minus the row mean, the fourth is the row mean, & the fifth is the standard deviation of the original features.
This would make sense if the three have sầu the same units (if they"re all accelerations at the same scale, this works), and so you want a separate measure of how much it"s being accelerated at all & how much it"s being accelerated in a particular direction.
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edited Sep 25 "16 at 16:12
answered Sep 25 "16 at 16:10
Matthew GravesMatthew Graves
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Mean centering is one of many related techniques lớn preprocess data for downstream analysis in multivariate methods.
It might sound odd at first, but it means exactly what it says: the vector has a mean of zero. In pseudocode, (sum(vector) / len(vector)) == 0.
In multivariate data, this typically is applied along each column in a dataset so each column can be more easily compared to another within a similar range of data. After mean centering, each row only includes how it differs from the average sample from that variable in the original data. Typically, samples are also scaled to have unit variance as well, allowing you to more readily compare the data across continuous variables with different ranges.
For example, if you had a dataset of patients with variables height, weight, age, household_income, despite each variable being a continuous value each of these variables will be in different range. Height might be between 60 -- 75 inches, weight between 100 -- 300 lbs, và so on.
Why vì all this? Removing the mean và standardizing the variance will help downstream methods not "learn" the mean and variance of your data, making it easier to find relationships between variables. Many assume that your data is centered / scaled / normalized in some way & will behave sầu poorly if you don"t do so.