Questions tagged [matrix]

A matrix is a collection of numbers arranged into a fixed number of rows and columns.

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ML for data processing. What are the options?

Currently I am working on improving a stage on a data processing pipeline. The source data has a large number of fields and is getting normalized into a simpler entity. This entails that in many cases ...
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Represent Neural Network as matrix calculation (Transformer Feed Forward NN)

for better understanding, I would like to represent the calculations in a neural network with one hidden layer and one output layer as a matrix calculation. The hidden layer has 3072 neurons, the ...
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Matrix multiplication

I have downstream gradient for every $sample$ (each row for every $x_i$) $$ \begin{bmatrix} 0.0062123 & -0.00360166 & -0.00479891 \\ -0.01928449 & 0.01240768 & 0.01493274 \\ ...
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Inverting a matrix using a convolutional neural network

Just for a fun exercise, I am trying to invert a matrix, say size 28x28 (or even 5x5) with a neural network. The way I approached this (quite naively) is as follows: I built a fully convolutional ...
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Attention transformation - matrices

Could somebody explain which matrix dimension should be found here - K? and if it is for example 3X3, should I use just 9?
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Efficient method of performing within matrix similarity

I want to compute a similarity comparison for each entry in a dataset to every other entry that is labeled as class 1 (excluding the current entry if it has a label of 1). So, consider a matrix of ...
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Reducing dataset before computing similarity matrix

I'm writing my thesis and am trying to calculate a similarity matrix of houses. I currently have a dataset of 500,000 houses that I need to calculate the similarity between. I.e. I need to calculate ...
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Correct dimensions of a Siamese network Input array

I have an image dataset where the folder structure is as follows- there are 900 folders (all of which will be classes) and in each folder, we have a varying number ...
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18 views

How do I extract the kernel matrix for a classifier created using `sklearn.svm.SVC`?

I am currently using the kernels that come with sk-learn support vector machine library. How do I extract the kernel matrix for a classifier created using ...
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Efficient way to create matrix that shows if data exits per day [closed]

So I have a dataset containing different ID's and the time the data was created. ...
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22 views

How to create a matrix in excel

I'm solving a system of equations in excel on a rolling data series. In each row is the data for a matrix that I need to invert, but I'm having a difficult time creating the matrix inline. I will do ...
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3answers
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converting array to a true/false matrices

I have a data set where each record is a json document with a label, and an array of signals. The signals will vary for each record: ...
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23 views

Neural Net that memorizes what it sees in order?

I'm sorry for this weird question, I know ML is about generalization but I have a specific use case where I'd like to build a neural network or really just a matrix, that memorizes everything it sees ...
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What is the formula used by np.dot?

Dot products are pretty simple for 1- or 2-dimensional arrays, but anything beyond that is incomprehensible to me. I tried looking into numpy‘s dot function but the ...
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Adding an extra dimension in weight matrix in Tensorflow

I was reading styleGAN2 code, in the networks_stylegan2.py file at line 95, they have added an extra dimension in the weight matrix for incorporating mini-batch. What I know is that TensorFlow can ...
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12 views

Reinforcement learning example when the action is a matrix

I am working on solving a problem with reinforcement learning which has to find the optimal matrix that maximize the reward. I am not able to see how I can formulate this problem as I have practiced ...
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16 views

Learning affinity among features

A batch of semantic objects in the image (lesions in CT scans) are represented in feature space, $X_{B \times C}$. I want to represent the whole batch in a single vector, $1 \times C$, in order to ...
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29 views

Toeplitz matrix in convolution neural network problem

Instead of multiplying the kernel with input vector iteratively, the convolution operation could be written as matrix multiplication. Infact this is how convolution operation is implemented ...
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1answer
105 views

PCA, covariance, eigenvector matrix and rotation [closed]

I am following the Coursera NLP specialization, and in particular the lab "Another explanation about PCA" in Course 1 Week 3. From the lab, I recovered the following code. It creates 2 ...
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Why is this equation converted to matrix form in this way? Is it possible to multiply an inverse matrix with a vector?

I have been banging my head on wall for days trying to decode this equation. please help me out with this... Below is the equation (consider $x$ as $\Delta x$, and $y$ as $\Delta y$): $x = - \eta(Id-\...
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68 views

What does an image pose (camera to world) mean?

I have 1000 2D images of a 3D scene. For each image, I have pose (camera to world) as follows: ...
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1answer
35 views

How to get used to matricial/vectorial operations?

I came to data science/machine learning from another background in computer science and i feel that i'm lacking of experience with matricial/vectorial operations. Python or Matlab, for instance, ...
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1answer
26 views

Matrix notation in Sutton and Barto

On pg. 206 of Barto and Sutton's Reinforcement Learning, there is a curious statement about the result of a scalar product: As I interpret it, A is the expectation of a scalar product of two d-...
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63 views

How can I get a value of context vector in GPT?

I'm a newbie in NLP and I'm now stuck in GPT. The question I'm struggling with is related to a term 'context vector' It says in the following (sorry that the material provided is written in korean) ...
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Sparse Covariance Selection

I was reading this article https://www.di.ens.fr/~aspremon/PDF/CovSelSIMAX.pdf, whose goal is to estimate the covariance matrix from a the sample covariance matrix drawn from a distribution $X$. ' ...
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1answer
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Conventions for dimensions of input and weight matrices in neural networks?

Im currently learning neural networks and I see conflicting decsriptions of the dimensions of weight and input matrices on the internet. I just wanted to know if there is some convention which more ...
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2answers
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what is the meaning of $\mathbb{R}^{768\times (768 * 2)}$?

Hi I'm an undergraduate student interested in Machine Learning. I was reading a paper from ICLR 2020 and came a cross a weird looking vector dimensions. Can anyone tell me what this means?? $\mathbb{R}...
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1answer
963 views

What types of matrix multiplication are used in Machine Learning? When are they used?

I'm looking at equations for neural networks and backpropagation and I see this symbol in the equations, ⊙. I thought matrix multiplication of neural networks always involved matrices that matched ...
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1answer
34 views

Can all known ML algorithms be written as a sequence of matrix operations?

I keep hearing that machine learning is just linear algebra. Does that mean all known (and all possible?) ML algos, from random forest, to support-vector machines, to recursive neural networks, can ...
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1answer
146 views

How to create a matrix from two given vectors in R in RStudio?

Suppose, $c(1, 2, 3, 4)$ and $c(2, 4, 5, 6)$ are two vectors. Then in R or RStudio, How to create a $4\times 2$ matrix from these two vectors? Also, how to add another vector $c(8, 9, 10, 11)$ ...
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1answer
703 views

Trying to understand the result provided by np.linalg.norm function in numpy (normalisation)

I'm new to data science with a moderate math background. I'm playing around with numpy and can across the following: So after reading np.linalg.norm, to my ...
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1answer
87 views

How do I use matrix math in irregular neural networks such as those generated from neuroevolution (NEAT)?

I understand how to structure the matrix when every node in a layer is fully connected to every node in adjacent layers and I understand that in "irregular" neural networks I can just process each ...
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What is the kernel matrix used for in the kernel trick?

I have $n$ linearly inseperable datapoints, $x_1 \dots , x_n$. I use the kernel trick to map and compute the dot product in higher dimensions (without actually mapping / transforming the data). ...
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1answer
233 views

How to compute Hessian matrix for log-likelihood function for Logistic Regression

I am currently studying the Elements of Statistical Learning book. The following equation is in page 120. It calculates the Hessian matrix for the log-likelihood function as follows \begin{equation} ...
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Why don't the dimensions in this linear regression equation match up?

I'm going through an article on linear regression, and they give the following formula for computing estimates: The convention is that all vectors are column vectors. So if ...
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1answer
139 views

tensorflow pseudo inverse doesn't work for complex matrices!

The Tensorflow documentation here says that: tf.linalg.pinv is ''analogous to numpy.linalg.pinv. It differs only in default value of rcond''. However, tf.linalg.pinv requires the matrix to ...
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1answer
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Recommender system that matches similar customers with similar highly rated products?

I have a dataset of 1,000 customers that bought 20 distinct phones and rated them 1-5. I have several demographic attributes for these customers (gender, age). My website offers 100 distinct devices, ...
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170 views

Scipy Sparse vstack memory error

I have a bunch of scipy matrices (of the same #columns) loaded from disk. I want to combine them into one scipy sparse matrix. I am using scipy sparse vstack method. I am able to load the ...
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Question regarding: Vectorization Math of Backpropagation in a Neural Network

Formula: These are the formula I use for backpropagation from Brilliant: Question: If we consider a Neural Network with the structure (3,2): And we would start calculating the derivative (for 1 ...
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1answer
59 views

List of Correlations to Correlation Matrix [closed]

I am using python to do some data analysis and I need to represent the following table as a correlation matrix. The correlation value is a value between -1 and 1. ...
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1answer
2k views

Normalize / Standardize in a Random Forest?

If I have a matrix of co-occurring words in conversations of different lengths, is it appropriate to standardize / normalize the data prior to training? My matrix is set up as follows: one row per ...
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Linear Regression Error in feature matrix step

I'm trying to code the design function used in linear regression using numpy and I get this error: Traceback (most recent call last): File "C:\Users\visha\AppData\Local\Continuum\anaconda3\lib\...
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How do I generate a laplacian matrix for a graph dataset?

If I have a dataset in a csv that looks like the one shown below. How do I convert this into a laplacian matrix using Python?
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2answers
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Removing # from header

I have a $(418,2)$ matrix and I want to convert it to csv. so I write: np.savetxt('titanic1.csv', Sol, fmt='%.2f', delimiter=",",header="PassengerId,Survived") ...
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Transformation of matrix with missing values for hierarchical clustering

Comparing different variables, I got a matrix with lots of missing values. How do I have to transform the matrix below for hierarchical clustering? What I have already tried: ...
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1answer
4k views

Calculating cosine similarity between 3D arrays using Python

I have two matrices with multiple columns and three rows each. I calculated the cosine similarity (sklearn) but it gives the result as a matrix. How can I obtain one single value? The matrices are the ...
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2answers
939 views

Is there a difference between np.matrix(np.array([0,0])) and np.matrix([0,0])?

I was reading this code, for implemnting linear regression from scratch: ...
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1answer
2k views

Computation of kernel matrix using radial basis kernel in svm

I want to compute a kernel matrix using RBF on my own. The training data is multidimensional. My query is whether we will apply $$e^{-\gamma(x-y)^2}$$ for each dimension and then sum the values across ...
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How does on test regression for a subspace or matrix factorization?

I've recently been reading a lot of papers and watching a lot of videos on both subspace learning, and matrix factorization. One thing is particularly eluding me though - how does any of this get ...