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What are eigenvectors and eigenvalues?
Introduction Eigenvectors and eigenvalues have many important applications in computer vision and machine learning in general. Well known examples are PCA (Principal Component Analysis) for...
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Why divide the sample variance by N-1?
Introduction In this article, we will derive the well known formulas for calculating the mean and the variance of normally distributed data, in order to answer the question in the article’s title....
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How to draw a covariance error ellipse?
Introduction In this post, I will show how to draw an error ellipse, a.k.a. confidence ellipse, for 2D normally distributed data. The error ellipse represents an iso-contour of the Gaussian...
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The Curse of Dimensionality in classification
Introduction In this article, we will discuss the so called ‘Curse of Dimensionality’, and explain why it is important when designing a classifier. In the following sections I will provide an intuitive...
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A geometric interpretation of the covariance matrix
Introduction In this article, we provide an intuitive, geometric interpretation of the covariance matrix, by exploring the relation between linear transformations and the resulting data covariance....
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Feature extraction using PCA
Introduction In this article, we discuss how Principal Component Analysis (PCA) works, and how it can be used as a dimensionality reduction technique for classification problems. At the end of this...
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Deep learning for long-term predictions
Detecting versus predicting At Sentiance, we use machine learning to extract intelligence from smartphone sensor data such as accelerometer, gyroscope and location. We’ve been doing this for quite a...
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Hybrid deep learning for modeling driving behavior from sensor data
Learning Driver DNA Usage based insurance solutions where smartphone sensor data is used to analyze the driver’s behavior are becoming prevalent these days. However, a major shortcoming of most...
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