Questions tagged [feature-extraction]
For questions related to the concept of feature extraction, which is a set of techniques used to derive or create features from the existing set of features. Feature extraction is different from feature selection, which is used to select a subset of the existing features.
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how to determine the number of units for dense layer for transfer learning?
I'm using MobileNetV2 for classification, and I want to add dense layers(i remove the last layer of the MobileNetV2 model). How do I choose the number of units for the dense layer after obtaining the ...
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the best choice to reduce a features vector
i have 1200 features highly correlated , and i want to reduce those number of features
so the best choice is use feature selection or dimensionality reduction? and which method is the best in this ...
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Sampling dollar bars for ML model of multiple tickers
I have a Neural Network model that provides predictions for the future returns of a portfolio comprising stocks and cryptocurrencies. The original model operates on standard time bars (sampled at a ...
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Feature vector representation of probability distribution
I have a series of multiple probability distribution like this:
[
[0.2, 0.3, 0.5],
[0.1, 0.2, 0.7],
...
]
Do you have any suggestions how I can represent this ...
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Can manual feature extraction be considered a part of a learning algorithm?
A learning algorithm is a tuple $(\mathcal{H}, \mathcal{O}, \mathcal{L})$ where $\mathcal{H}$, $\mathcal{O}$ and $\mathcal{L}$ are the hypothesis class, optimizer and loss function respectively.
We ...
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What is $\mathbf{S}$ (sample covariance matrix) in image compression based on PCA?
If the feature vector is $\mathbf{x} \in \mathbb{R}^{d}$,
then to apply PCA we first need to construct the "sample covariance matrix)
\begin{align*}
\underbrace{\frac{1}{N}\sum_{i=1}^N(\mathbf{x}^...
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Is geodesic distance between two similar photos less than the Euclidean distance between them? If so, why?
This is from a ML book:
"Principal component analysis, which we discussed in section 6.3, works
when the data lies in a linear subspace. However, this may not hold in
many applications. Take, for ...
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Why is k=1 in linear discriminant analysis for two classes?
With two classes, why does Linear Discriminant Analysis (LDA) consider only
projecting onto one dimension (k=1)? Normally, even with 2 classes, you can consider
projecting the d-dimensional original ...
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How to extract features from patterns in time series data
I have a time series welding data I wanted to create a model which can predict some weld parameters but extracting those parameters from time series data is being so difficult.
Currently I tried ...
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Why is the feature direction chosen in the direction associated with largest eigenvalue of $\Sigma_T$ in case of more than two classes?
Why is the feature direction chosen in the direction associated with largest eigenvalue of $\Sigma_T$ in case of more than two classes? Please see the following.
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Can Vision Transformers be used to extract features?
Can Vision Transformers be used to extract features, just like with VGG ?
I am interested in using this vision transformer in extracting features (https://huggingface.co/google/vit-base-patch16-224)
...
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Unclear points on feature extraction for a set of scenarios
The following is an example from a book (An Introduction to Pattern
Recognition and Machine Learning by P. Fieguth, page 85) on feature extraction and selection.
Please consider the following figure.
...
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Can a concept/feature be represented using more than one layer of a Neural Network?
I was reading Goodfellow. At the start of the text it was mentioned that there are two ways to represent depth of a deep neural network. One is using the depth of the computation graph and the other ...
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How translation invariance is achieved in CNNs?
I am trying to understand how translation invariance is achieved in CNNs. For example, consider the following simple binary classification problem: predicting whether the letter that appears on an ...
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Can we use bounding box cropping to avoid shortcut learning (achieve explainable AI)?
Deep neural networks sometime use shortcut features (pseudo correlation) to predict.
For example, in cat-dog classification, the network may use the background information (e.g. floor, grass) as a ...
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Software to auto-mark facial points on images [closed]
I'm not interested in facial recognition per se, but my question is related. Is there a software (commercial, Github etc), which can mark, with color dots, the same reference points on a face (e.g. ...
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How do i approach creating a masked auto-encoder for feature extraction
I trained Masked Autoencoder-based models in order to use the encoder as a backbone for another task. Pretraining has been done in a Self-Supervised manner on image data. Now that it comes to my ...
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Difference between using transformer for multi-class classification and clustering using last hidden layer
My data is a collection of URLs where I am interested in categorizing them into multiple groups.
At the moment, I am using a pre-trained transformer model and fine-tuning it according to my data with ...
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Normalisation in feature extraction using pre-trained model
I have a dataset with medical images. I want to implement a network for super-resolution using GANs. One of the criteria of the optimisation is a perceptual loss. For that I will use a pretrained vgg ...
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Can DeepSort be made to track objects beside people?
As far as my understanding goes, the model used for feature extraction in DeepSort is specified as the first argument of the function create_box_encoder in the file ...
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How does a Machine Learning model predict this classification problem?
Let’s imagine we want to create a simple Sentiment Analysis model using Machine Learning not Deep Learning algorithms, so we need to have a set of handcrafted features for this classification problem.
...
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What's the purpose of "feature extraction using a pretrained model"?
I'm sorry this is such an elementary question because I'm an AI beginner.
In this link, it says
One thing that is commonly done in computer vision is to take a model trained on a very large dataset, ...
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Feature Extraction for timeseries temperature signal [closed]
i have two temperature signals from which one is sensitive toward a specific event. I would like to know what other features can be useful to extract apart from:
Angles (between the two).
Slopes ( ...
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How to extract the high-level features of YOLOv5?
I am faimilar with extracting the high-level features from any pretrained model for classification problem such as ResNet version, VGG, etc. It is easy to extract the features because there is a fully ...
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SCINet: how does interactive learning work?
i'm having some trouble understanding how does the basic building block of a SCINet works. In the paper the author describes the SCI-block with the following figure:
In which $\phi$, $\theta$, $\eta$ ...
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How to use image feature extraction as input to another model?
I have a dataset with the following info
Image1 x1 x2 x3 y
Image2 x1 x2 x3 y
...
Where x1, x2 & x3 are categorical features. My goal is to extract features from the images and use those features ...
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Order of features learned by DNNs during training?
I'm looking for papers probing into the question of what features get learned when (or equivalently what subproblems get "solved" when) during the training process. For example, a paper ...
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Pseudo Label Generation for Generative Cooperative Learning
I am trying to implement this paper for unsupervised video anomaly detection.
The gist of the paper seems to be:
Create a dataset for an unsupervised setting, by mixing up the train and anomalous ...
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491
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How to calculate a meaningful distance between multidimensional tensors
TLDR: given two tensors $t_1$ and $t_2$, both with shape $(c,h,w),$ how shall the distance between them be measured?
More Info: I'm working on a project in which I'm trying to distinguish between an ...
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Make an NN utilize other NNs as part of its decision process
Suppose I have a NN that learns to predict the time it takes a robot to move between two jobs. That's three inputs (for starters): robot, job A, job B. Not all robots travel at the same speed, and ...
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How to convert color information to 1D feature vector?
We are making a classification model that takes a clip of a movie as an input and predicts who the director is. Roughly speaking, it will be a model that understands film directors' unique style.
We ...
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A technique of aggregating many input images to a single representation of the relevant features within
I have a few thousand images and I would like to generate a representation of the foreground patterns within - a composition of all of its features, so to speak. In simple terms: take 10000 images of ...
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Is the phrase "Feature Pyramid Network" refer to CNN only?
"Feature Pyramid Network" is a network that is used for feature extraction. Since it is pyramid in shape, it might be called so.
Consider the following excerpts from two different sources
#1
...
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Derive information for sub-scoring from one scoring model
I am currently working in Python with a random forest algorithm to perform a scoring. My output is binary.
The idea now is to derive sub-scores from the above model that give an opinion on different ...
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Bag of Tricks: n-grams as additional features?
I've been playing with PyTorch's nn.EmbeddingBag for sentence classification for about a month. I've been doing some feature engineering, playing with different ...
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In SIFT, how is the coordinate system being rotated?
I need to understand how SIFT calculates the descriptors for the keypoints.
Intuitively, I understand that it takes each keypoint, calculates the gradients for each pixel in a neighborhood of the ...
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How is the bias added after the convolution in a CNN?
I'm having trouble understanding how bias is added to the feature extraction convolution. I've seen people either refer to the bias as a single number that changes per filter or the whole matrix that ...
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What is the difference between feature extraction and fine-tuning in transfer learning?
I'm building a model for facial expression recognition, and I want to use transfer learning. From what I understand, there are different steps to do it. The first is the feature extraction and the ...
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Where do the feature extraction and representation learning differ?
Feature selection is a process of selecting a subset of features that contribute the most.
Feature extraction allows getting new features that are not actually present in the given set of features.
...
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Is it a good practice to pad signal before feature extraction?
Is padding, before feature extraction with VGGish, a good practice?
Our padding technique is to find the longest signal (which is loaded .wav signal), and then, in ...
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How is a ResNet-50 used for deep feature extraction?
I'm trying to implement the vehicle re-identification model described in https://arxiv.org/pdf/2004.06271.pdf.
My question focuses on Section 3.2 of the paper, which uses a ResNet-50 for deep feature ...
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Advice required for identifying bone fragments in CT-scans using STL Files (3D image segmentation)
I am working on a project related to automating the procedure of manually segmenting some bones in CT scans and hopefully if everything goes alright in this stage, move on to do something more with ...
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How can Image Caption work?
I have two models and a file contains captions for images. The output of model 1 is .pkl files that contain the features of the images. Model 2 is the language model that will be trained with the ...
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Can I use the SIFT feature detector on data other than images?
I know how to use SIFT algorithm for images but I never use it for other kinds of data. I have tabular data (x, y, z, time) where x,y,z is the joint position along x, y, z coordinates. Now, can I ...
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When is it necessary to manually extract features to feed into the neural network rather than providing raw data?
Usually, Neural Networks uses raw data. You do not need to extract features manually. NN's can find & extract good features which is a pattern of an image, signal or any kind of data. When we ...
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What is the difference between feature extraction with or without data augmentation?
Here's an extract from Chollet's book "Deep Learning with Python" about using pre-trained CNN to predict class from a photo set (p. 146):
At this point, there are two ways you could proceed:...
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How should I use deep learning to find the rotation of an object from its 2D image?
I have 6600 images and I am supposed to know the rotation of the object in each image. So, given an image, I want to regress to a single value.
My attempt: I use Resnet-18 to extract a feature vector ...
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Statistical method for selecting features for classification
I'm working on a classifier for the famous MNIST handwritten data set.
I want to create a few features on my own, and I want to be able to estimate which feature might perform better before actually ...
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Neural Network for locating shifting resonant frequencies
I have multiple FFT's taken from a sample at different pressures, through different analysis I can see that the resonant frequencies are shifting in the spectrum for each FFT at a different pressure.
...
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Which type of feature extractor do you suggest to classify sensor data?
I have IMU (Inertial Measurment Unit- 6 axis) sensor data. The sensor attached on a car and 7 different drivers wipe on same path. I want to extract features and classify drivers. Which type of ...
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