Questions tagged [linear-algebra]
A field of mathematics concerned with the study of finite dimensional vector spaces, including matrices and their manipulation, which are important in statistics.
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How to add extra constraints to an equation？
Background：
I have an equation which looks like as follows:
$W \times P = R$
$\left[\begin{array} &{1}&{0}&{0}&-\frac{w_{1}}{w_{o1}} &\dots &{0} &-\frac{w_{1}}{w_{0} } \\...
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1answer
104 views
How to “reshape” into square matrix for numpy.linalg.solve()?
I'm trying to find the intersection of lines $y=a_1x+b_1$ and $y=a_2x+b_2$ using numpy.linalg.solve(). What I can't get my head around is how to correctly make $A$ ...
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0answers
64 views
Connection between piecewise linear basis functions and RELU activation function
ReLU activation is defined as follows
$$\sigma(x)=\max(0, x).$$
Let's assume that I have deep network of 1 hidden layer, than output from my layer has form
$$ f(x)= \sigma(Wx +b), $$
where matrix W ...
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1answer
51 views
Optimizing vector values for maximum correlation
I'm new to ML, linear algebra, statistics, etc. so bear with me on the terminology...
I’m looking to find a vector that produces the maximum correlation for the relationship between 1) all ...
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1answer
23 views
How can positional encodings including a sine operation be linearly transformable for any offset?
In the paper "Attention is all you need" the authors add a positional encoding to each token in the sequence (section 3.5). The following encoding is chosen:
$ PE(pos, 2dim) = sin(pos / 10000 ^ {2dim/...
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0answers
73 views
Can we think of neurons as maps between matrices?
Usually when we think about neurons, we imagine that they enact some kind of map between real numbers. For example, a neuron might take in real numbers $x_{i}$ and weight them with parameters $W_{ij}$,...
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0answers
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Structures for incorporating linear functions into a nonlinear optimization problem
I'm working on a problem which naturally involves both linear and nonlinear operations, and I'd like some help understanding the best way to combine these into a neural network framework. To be more ...
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0answers
23 views
Dimensionality reduction categories
According to what I found, dimensionality reduction has two types feature selection and feature extraction . In feature extraction, we find PCA, LDA ,LLE , ISOMAP, etc.. In other works i find random ...
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1answer
102 views
Machine learning PhD Interview technical questions [closed]
I'm Software Engineer who applied to grad school for Machine Learning/Computer Vision PhD and currently waiting for interview calls. I'm brushing up Linear algebra/ ML topics. What kind of technical ...
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2answers
238 views
How can I implement tangent distance for k-nearest neighbor in python/scikit-learn?
My ultimate aim is to have a function which I can feed into scikit-learn's NearestNeighbor class as a custom metric parameter.
...
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0answers
33 views
Linear algebra library for c++
I have been trying to find the substitute of numpy and perform some linear algebra using c++ and here's a list of libraries I have encountered:
Eigen
Armadillo
Dlib
GNU Scientific library
Please ...
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4answers
54 views
Statistics Before Linear Algebra?
I know this is an opinion-based question and will be closed but this is the only place I know that can answer it reasonably and it is a very important matter to me.
I am pursuing a machine ...
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3answers
1k views
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1answer
33 views
Can I use regression to solve a multiple equation problem
I'm working on a problem which is a multiple equation. I have a group of people and each person in the group is working on different tasks (e.g. n tasks in total). Each person in this group is working ...
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1answer
31 views
On minimizing matrix norm (AB-C)
Given A, B and C are matrices with dim(A) = m x n, dim(B) = n x p and dim (C) = m x p, the problem asks to evaluate
I need to learn $$\tilde{A}$$ such that $$\min_{\tilde{A}}||\tilde{A}^TB-C||$$
and ...
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1answer
46 views
Least Squares Regression $Ax=b$ when $A$ is fixed and $b$ is varied
The typical setting for least squares regression (or over-determined linear system) for $Ax=b$ is to solve $x$ given $A$ and $b$.
In other words, $A$ and $b$ are fixed when we solve the problem.
My ...
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2answers
25 views
Are euclidian vectors and unit vectors same thing? [closed]
Consider this statement : Let the field K be the set R of real numbers, and let the vector space V be the Euclidean space R3. Consider the vectors e1 = (1,0,0), e2 = (0,1,0) and e3 = (0,0,1). Then any ...
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1answer
114 views
RNN: why Wx + Uh instead of W[x,h]
Traditionally, a state for RNN is computed as
$$h_t = \sigma(W\cdot \vec x + U\cdot \vec h_{t-1} + \vec b)$$
For a RNN, why to add-up the terms $(Wx + Uh_{t-1})$ instead of just having a single ...
3
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2answers
46 views
Derivates with respect to a vector
Suppose I have an equation, $f = X^TY + \dots$ (a few more terms), where $X$ is a vector and $Y$ is a matrix of appropriate dimensions,
I want to know how can we take the derivative of $f \text{ w.r.t....
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2answers
97 views
How does “linear algebraic” weight training function work?
This answer shows that linear and polynomial function weights can be trained using this matrix operation:
$w = (X^TX)^{-1}X^Ty$
Therefore, algorithms such as gradient descent are not necessary for ...
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1answer
184 views
What exactly is the “hyperbolic” tanh function used in the context of activation functions?
I know the plot of $\tanh$ activation function looks like. I also know that its output has a range of $[-1, 1]$. Furthermore, I also know the it is defined as follows
$$
\tanh(x) = \frac{\sinh(x)}{...
5
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1answer
151 views
Closed form solution of linear regression via least squares using matrix derivatives
How is the closed form solution to linear regression derived using matrix derivatives as opposed to using the trace method as Andrew Ng does in his Machine learning lectures. Specifically, I am ...
3
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1answer
352 views
What is the use of additional column of 1s in normal equation?
Currently I am going through Normal Equation in Machine Learning.
$$
\hat\theta = (X^T \cdot X)^{-1} \cdot X^T \cdot y
$$
But when I see how they use this equation, I found they always add an ...
2
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0answers
48 views
How does SVD actually provide the recommendations? I seem to get conflicting answers
I am reading a text book that basically says the following:
Given a matrix A where A is USERS x ITEMS we can use SVD to decompose the matrix into:
$$A = U \times \Sigma \times V^T$$
Then we can take ...
1
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1answer
234 views
Is there a quick way to speed up ICP in python using a cached KD-tree
I am currently using ICP to match 2 point clouds. These point clouds evolve in time, so I have to repeat this process many times. I am using a standard KD tree from scipy for my nearest neighbor ...
5
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1answer
850 views
Deriving backpropagation equations “natively” in tensor form
Image shows a typical layer somewhere in a feed forward network:
$a_i^{(k)}$ is the activation value of the $i^{th}$ neuron in the $k^{th}$ layer.
$W_{ij}^{(k)}$ is the weight connecting $i^{th}$ ...
1
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1answer
311 views
Eigenvectors and eigenvalues for natural language processing
How are eigenvectors and eigenvalues can be applied/applicable to natural language processing problems ? Any examples ?
