Posts tagged with: feature extraction

ML Work-Flow (Part 3) – Feature Extraction

In this post, I’ll talk about the details of Feature Extraction (aka Feature Construction, Feature Aggregation …) in the path of successful ML. Finding good feature representations is a domain related process and it has an important influence on your final results. Even if you keep all the settings same, with different Feature Extraction methods you would observe drastically different results at the end. Therefore, choosing the correct Feature Extraction methodology requires painstaking work.

Feature Extraction is a process of conveying the given raw data into set of instance points embedded in a standardized, distinctive and machine understandable space. Standardized means comparable representations with same length; so you can compute similarities or differences of the instances that have initially very versatile structural differences (like different length documents). Distinctive means having different feature values for different class instances so that we can observe clusters of different classes in the new data space. Machine understandable representation is mostly the numerical representation of the given instances. You can understand any document by reading it but machines only understand semantics implied by the numbers. Continue Reading

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Machine Learning Work-Flow (Part 1)

So far, I am planning to write a serie of posts explaining a basic Machine Learning work-flow (mostly supervised). In this post, my target is to propose the bird-eye view, as I’ll dwell into details at the latter posts explaining each of the components in detail. I decide to write this serie due to two reasons; the first reason is self-education -to get all my bits and pieces together after a period of theoretical research and industrial practice- the second is to present a naive guide to beginners and enthusiasts.

Below, we have the overview of the proposed work-flow. We have a color code indicating bases. Each box has a color tone from YELLOW to RED. The yellower the box, the more this component relies on Statistics knowledge base. As the box turns into red[gets darker], the component depends more heavily on Machine Learning knowledge base. By saying this, I also imply that, without good statistical understanding, we are not able to construct a convenient machine learning pipeline. As a footnote, this schema is changed by post-modernism of Representation Learning algorithms and I’ll touch this at the latter posts.

 

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How does Feature Extraction work on Images?

Here I share enhanced version of one of my Quora answer to a similar question …

There is no single answer for this question since there are many diverse set of methods to extract feature from an image.

First, what is called feature? “a distinctive attribute or aspect of something.” so the thing is to have some set of values for a particular instance that diverse that instance from the counterparts. In the field of images, features might be raw pixels for simple problems like digit recognition of well-known Mnist dataset. However, in natural images, usage of simple image pixels are not descriptive enough. Instead there are two main steam to follow. One is to use hand engineered feature extraction methods (e.g. SIFT, VLAD, HOG, GIST, LBP) and the another stream is to learn features that are discriminative in the given context (i.e. Sparse Coding, Auto Encoders, Restricted Boltzmann Machines, PCA, ICA, K-means). Note that second alternative, Continue Reading

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