Articles and books on feature *extraction*

It seems that the question title and question details are not consistent. Can you fix the wrong one (I think the title)?

I think he wants to know about feature extraction (hence the tag). Unfortunately, apart from more general methods of dimensionality reduction this is fairly domain-specific (as far as I know). Feature extraction in speech, vision, and bioinformatics have (again, as far as I know) rather little overlap. What area are you interested in?

I know almost nothing about computational chemistry. You might want to look up protein prediction? Though there's probably stuff better suited to your purpose.

Do you mean feature engineering, what people do in NLP, which is thinking very hard about the problem domain and then writing simple pattern matchers that are known to either correlate or anticorrelate with the answer?

Do you mean feature extraction as performed by vision people, where you run lots of edge detectors, multiscale filters, and descriptor extractors (like SIFT), and then vector quantize these to get features for classification/clustering?

Or do you mean the deep learning kind of feature extraction, which is roughly domain agnostic and is based on training RBMs or autoencoders on top of enough other RBMs or autoencoders and then maybe training an SVM on the "higher-level" activations of the neurons of these deep neural networks?

@lmsasu: Sure. For the second alternative, search for SIFT, descriptors, edge detectors, feature bagging, etc, on google scholar or the archives of high-profile computer vision conferences. For the third alternative help yourself to the deeplearning.net reading list: http://deeplearning.net/reading-list/