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This is conditioning in the sense of conditional probability. The idea is that the authors have some "standard physically-inspired features". They are splitting the data up into bins based on the values of these features, and then training a model for each bin. They are then examining the differences between the models. Usually this is done to learn ...


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Non-correlation does not imply independence, that is, if two features are not correlated (i.e. zero correlation), it does not mean that they are independent. But (non-zero) correlation implies dependence (see https://stats.stackexchange.com/q/113417/82135 for more details). So, if you have non-zero correlation between two features, it means they are ...


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Data pre-processing and feature extraction are by far the most important part of any machine learning algorithm. It's even more important that the model you choose to do the classification. Unfortunately, pre-processing and feature extraction are completely different for each type of data. You need to play around with the data yourself to find out what ...


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The principal components (eigenvectors) correspond to the direction (in the original n-dimensional space) with the greatest variance in the data. The corresponding eigenvalue is a number that indicates how much variance there is in the data along that eigenvector (or principal component). Thus, feature 2 is the most important (based on ...


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In general, the expression "temporal feature" might refer to any feature that is associated with or changes over time. However, in the context of signal processing, a temporal feature might refer to any feature of the data before being transformed to the Fourier, frequency or spectral domain, using the Fourier transform. In this context, the domain ...


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Introduction Bag-of-features (BoF) (also known as bag-of-visual-words) is a method to represent the features of images (i.e. a feature extraction/generation/representation algorithm). BoF is inspired by the bag-of-words model often used in the context of NLP, hence the name. In the context of computer vision, BoF can be used for different purposes, such as ...


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Simply said, there is no specific "meaning" to the features generated. They are simply features that are fitted through math and calculus, and nobody knows what they represent exactly, and will never knows. However we can run PCA (Principal Component Analysis) to see which feature is the most "important" of all, aka which feature affects the most in the ...


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Now I want to check if I can predict B directly from A, since, in my understanding, this would mean that info on B is already inside A. This will help inform you how much redundancy there is between A and B. However, even if you can predict B with 100% accuracy from A, you may still be better off using A+B (instead of A alone) to predict C. If I get good ...


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You want to say bag-of-visual words not bag-of-words, this technique was classicly used in Computer vision before the introduction of neural networks or some more advanced classical tehcniques such us VLAD or Fisher Vectors, in any case it is a good tehcnique to use, but it is not the state-of-the-art today, and I won't recommand you to use for a real life ...


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Pragmatically you could use the discriminatory from a GAN for outlier detection. Ideally you'd start collecting fakes now and do a normal model on both good and bad cases. In the absence of that you can create a GAN to create realistic looking fakes on only real cases and then take the discriminator from that GAN to flag real-life cases for manual checks. ...


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The problem which you have is a classification problem. You assume a class "good users" and a distinct class "bad users". You want to train an AI to tell the two apart, but all your examples are "good users". Any reasonable AI will draw the logical conclusion from those examples: all users are good users. That's a 100% match for the training data.


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