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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Extracting features from multiple curves

I am building a model that predicts the SOH of a lithium ion battery. My data are from 600 battery charge cycles as follows: for each cycle I have 3 curves each of length 128 samples: voltage, current ...
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1 answer
49 views

Why CNN filters (kernels) are randomly initialized?

I learned that when CNN filters are defined, they are initialized with random weights and bias(Im not sure about bias). Then as learning step goes on, the weight values change and each filter makes ...
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1 answer
73 views

What is the meaning of "energy loss" in PCA?

Recently in a slide in about PCA (Principal Component Analysis) I saw a question: "How much is the data energy loss in PCA?&...
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1 answer
93 views

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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1 answer
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What are some good papers or resources for aspect extraction and opinion modelling from video or audio?

I am quite new to deep learning. I just finished the deep learning specialization by Professor Andrew NG and Deep Learning AI. Now, my professor (instructor) has advised me to look into some classic ...
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2 answers
82 views

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 ...
-2 votes
1 answer
59 views

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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0 answers
17 views

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 ...
1 vote
1 answer
52 views

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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18 views

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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1 answer
433 views

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 ...
1 vote
1 answer
72 views

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}^...
1 vote
2 answers
42 views

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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11 views

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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14 views

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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11 views

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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1 answer
271 views

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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50 views

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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1 answer
26 views

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 ...
2 votes
2 answers
63 views

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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19 views

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 ...
1 vote
0 answers
33 views

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 ...
0 votes
1 answer
31 views

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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0 answers
93 views

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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30 views

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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0 answers
20 views

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 ...
0 votes
2 answers
91 views

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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1 answer
138 views

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 ...
2 votes
1 answer
521 views

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 ...
0 votes
1 answer
70 views

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. ...
0 votes
1 answer
89 views

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 ...
4 votes
2 answers
562 views

Is it true that untrained CNNs can be used as feature extractors?

I've heard somewhere that due to their nature of capturing spatial relations, even untrained CNNs can be used as feature extractors? Is this true? Does anyone have any sources regarding this I can ...
0 votes
1 answer
34 views

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 ( ...
1 vote
0 answers
22 views

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 $\phi$, $\theta$, $\eta$ ...
2 votes
1 answer
368 views

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 ...
1 vote
0 answers
36 views

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 ...
3 votes
2 answers
264 views

Is such a captcha AI-resistant?

Let's say we have a captcha system that consists of a greyscale picture (of a part of a street or something akin to re-captcha), divided into 9 blocks, with 2 missing pieces. You need to choose the ...
1 vote
1 answer
58 views

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 ...
1 vote
1 answer
190 views

How to deal with Unix timestamps features of sequences, which will be classified with RNNs?

I want to use RNN for classifying whole sequences of events, generated by website visitors. Each event has some categorical properties and a Unix timestamp: ...
2 votes
2 answers
393 views

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 ...
1 vote
1 answer
507 views

How to choose a suitable threshold value for the Shi-Tomasi corner detection algorithm?

While implementing the Shi-Tomasi corner detection algorithm, I got stuck in deciding a suitable threshold for corner detection. In the Shi-Tomasi algorithm, all those points that qualify $\min( \...
1 vote
0 answers
97 views

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 ...
4 votes
1 answer
2k views

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. ...
2 votes
2 answers
4k views

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 ...
1 vote
1 answer
586 views

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 ...
1 vote
0 answers
30 views

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 ...
1 vote
1 answer
83 views

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 ...
0 votes
0 answers
420 views

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 ...
0 votes
1 answer
87 views

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 ...
1 vote
1 answer
90 views

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 ...