unsupervised-learning

Can anyone explain what the major differences between the prcomp and princomp functions are? Is there any particular reason why I should choose one over the other? In case this is relevant, the type of application I am looking at is a quality control analysis for genomic (expression) data sets. Thank you! doug There are differences between these two functions w/r/t the function parameters (what you can/must pass in when you call the function); the values returned by each; and the numerical technique used by each to calculate principal components. Numerical Technique Used to Calculate PCA In

I would like to design a deep net with one (or more) convolutional layers (CNN) and one or more fully connected hidden layers on top. For deep network with fully connected layers there are methods in theano for unsupervised pre-training, e.g., using denoising auto-encoders or RBMs . My question is: How can I implement (in theano) an unsupervised pre-training stage for convolutional layers? I do not expect a full implementation as an answer, but I would appreciate a link to a good tutorial or a reliable reference. This paper describes an approach for building a stacked convolutional autoencoder

suppose i have 10 individual observations each of size (125,59). i want to group these 10 observations based on their 2d feature matrices ((125,59)).Is this possible without flattening every observation to 125*59 1D matrix ? I cant even implement PCA or LDA for feature extraction because the data is highly variant. Please note that i am trying to implement clustering through self organizing maps or neural networks. Deep learning and neural networks are completely related to the question asked. Of course it is. Define an appropriate distance measure. Then compute the 10x10 distance matrix, and

I am using Convolutional Neural Networks (Unsupervised Feature learning to detect features + Softmax Regression Classifier) for image classification. I have gone through all the tutorials by Andrew NG in this area. ( http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial ). The network that I have developed has an : Input layer - size 8x8 (64 neurons) Hidden layer - size 400 neurons Output layer - size 3 I have learnt the weights connecting the input layer to the hidden layer using a sparse autoencoder and hence have 400 different features. By taking continuous 8x8 patches from any input

I have a very large amount of data in the form of matrix.I have already clustered it using k-means clustering in MATLAB R2013a. I want the exact coordinates of the centroid of each cluster formed.. Is it possible using any formula or anything else? I want to find out the centroid of each cluster so that whenever some new data arrives in matrix, i can compute its distance from each centroid so as to find out the cluster to which new data will belong My data is heterogeneous in nature.So,its difficult to find out average of data of each cluster.So, i am trying to write some code for printing the

In terms of artificial intelligence and machine learning, what is the difference between supervised and unsupervised learning? Can you provide a basic, easy explanation with an example? Davide Since you ask this very basic question, it looks like it's worth specifying what Machine Learning itself is. Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal" algorithms it is the data that "tells" what the "good answer" is. Example: a hypothetical non-machine learning algorithm for face detection in images would try to define what a face is (round skin-like-colored