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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questions with no upvoted or accepted answers
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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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Visualisation for Features to Predict Timeseries Data
I have a course assignment to use an LSTM to predict the movement directions of stock prices. One of the things I am asked to do is provide a visualization to compare the predictive powers of a set of ...
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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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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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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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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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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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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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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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Feature extraction for exponentially damped signals
I am looking into exponentially damped signals where it is a stationary signal (after implementing the Adfuller statistical test) and I would like to look into how can I extract meaningful features ...
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Plot class activation heatmap of Caffe Model in Python
Given the following 3 research papers, the authors have shown different heatmap graphical representations for features of the trained CNN models:
On the performance of Convnet feature for place ...
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CBIR Evaluation on contextually different data
How good would a CBIR system trained on a dataset, for example, DELF, trained on the Google Landmarks dataset, perform when evaluated on a contextually different dataset such as the WANG or the COREL ...
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Learning Features from a Pre-trained Network
I am currently working on learning the features provided by a pre-trained network for image retrieval. Currently I take the features provided by the pre-trained network, use global max pooling to ...
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Can we use Autoencoders for unsupervised CNN feature learning?
I searched through the internet but couldn't find a reliable article that answers this question.
Can we use Autoencoders for unsupervised CNN feature learning of unlabeled images like the below
and ...
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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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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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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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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 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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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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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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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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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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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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What are examples of commonly used feature and readout maps?
It is well-known that deep feedforward networks can approximate any continuous function from $\mathbb{R}^k$ to $\mathbb{R}^l$, (uniformly on compacts).
However, in practice feature maps are ...
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