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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Gradient descent formula implementation in python

So I recently started with Andrew Ng's ML Course and this is the formula that Andrew lays out for calculating gradient descent on a linear model. $$ \theta_j = \theta_j - \alpha \frac{1}{m} \sum_{i=1}...
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2answers
551 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 ...
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What does sparsely compute mean?

I heard someone say a neural network needs to sparsely compute the output. I get what compute means, I get what a sparse matrix is, but what does sparsely compute mean?
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PCA formulation - Deep Learning book by Ian Goodfellow

I am reading this deep learning book by Ian goodfellow. In the PCA formulation in the first chapter i.e Linear Algebra, he mentions the following: we need to choose the encoding matrix D. To do so,...
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28 views

Eigen Decomposition of Data Matrix for PCA

In PCA we Eigen decompose the covariance matrix, not data matrix, Is it because most data matrices are non-square. If they were, isn't is correct to eigen decompose data matrix than the covariance ...
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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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206 views

Finding linear transformation under which distance matrices are similar

I have n sets of vectors, where each set S_i contains k vectors in ...
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11 views

Chaining bias terms in backprob

let's say I have a few linear layers $l_1 \dots l_n$: $y=I(\dots I(IX + b_1) + b_2) \dots +b_n)$ where $n$ is sufficiently large and $I$ is the (nonparametric) identity matrix. The gradient for $b_n$...
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I can't understand polynomial in the book

I'm reading a book called Bishop - Pattern Recognition and Machine learning. I came across the following equation, in which I don't understand what $W$ stands for. So, what is $W$?
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199 views

Mathematical formulation of Support Vector Machines?

I'm trying to learn maths behind SVM (hard margin) but due to different forms of mathematical formulations I'm bit confused. Assume we have two sets of points $\text{(i.e. positives, negatives)}$ one ...
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1answer
61 views

Why does np.linalg.eig produce an opposite-signed eigenvector?

I am learning SVD by following this MIT course. In this video, the lecturer is finding the SVD for $$ \begin{pmatrix} 5 & 5 \\ -1 & 7 \end{pmatrix}, $$ which involves finding the ...
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Performant alternatives to Matrix package in R that requires minimal effort by end users to install

Background I'm going to be releasing a package for R that does calculations involving extremely large sparse matrices (1,000,000 x 1,000,000 is the minimum for what we consider useful). For this ...
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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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1answer
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Linear regression with white Gaussian noise

I am new to machine learning , so this question may sound fundamental. My task is to estimate the parameter vector of the equation with the least squares method: $y = \theta_0 + \theta_1x + \theta_2x^...
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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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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 ...
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517 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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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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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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1answer
220 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)}{...
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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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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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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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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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313 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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4answers
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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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2answers
47 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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How does tensor product/multiplication work?

In Tensorflow, I saw the following example: ...
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1answer
273 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 ...
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1answer
33 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
50 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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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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187 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 ...
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2answers
101 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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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 ...
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1answer
357 views

Eigenvectors and eigenvalues for natural language processing

How are eigenvectors and eigenvalues can be applied/applicable to natural language processing problems ? Any examples ?
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965 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 $i^{th}$ ...