I am trying to cluster a dataset using an encoder and since I am new in this field I cant tell how to do it.My main issue is how to define the loss function since the dataset is unlabeled and up to know, what I have seen from bibliography they define as loss function the distance between the desired output and the predicted output.My question is since that I dont have a desired output how should I implement this?
You can use an auto encoder to pre-train your convolutional layers, like it described in my question here with usage of convolutional autoencoder for images
As you can see form code, loss function is Adam with metrics accuracy and dice coefficient, I think you can use accuracy only, since dice coefficient is image-specific
I’m not sure how it will work for you, because you hadn’t provided your idea how you will transform your bibliography lists to vector, perhaps you will create a list for bibliography id’s sorted by the cosine distance between them
For example, you can use a set of vector with cosine distances to each item in a bibliography list above for each reference in your dataset and use it as input for autoencoder
After encoder will be trained, you can remove the decoder part from your model output and use as an input for one of unsupervised clustering algorithms, for example, k-mean. You can find details about them here
Related
I have a question on self-organizing maps:
But first, here is my approach on implementing one:
The som neurons are stored in a basic array. Each neuron consists of a vector (another array of the size of the input neurons) of double values which are initialized to a random value.
As far as I understand the algorithm, this is actually all I need to implement it.
So, for the training I choose a sample of the training data at random an calculate the BMU using the Euclidian distance of sample's values and the neuron weights.
Afterwards I update it's weights and all other neurons in it's range depending on the neighborhood function and the learning rate.
Then, I decrease the neighborhood function and the learning rate.
This is done until a fixed amount of iterations.
My question is now: How do I determine the clusters after the training? My approach so far is to present a new input vector and calculate the min Euclidian distance between it and the BMU . But this seems a little naive to me. I'm sure that I've missed something.
There is no single correct way of doing that. As you noted, finding the BMU is one of them and the only one that makes sense if you just want to find the most similar cluster.
If you want to reconstruct your input vector, returning the BMU prototype works too, but may not be very precise (it is equivalent to the Nearest Neighbor rule or 1NN). Then you need to interpolate between neurons to find a better reconstruction. This could be done by weighting each neuron inversely proportional to their distance to the input vector and then computing the weighted average (this is equivalent to weighted KNN). You can also restrict this interpolation only to the BMU's neighbors, which will work faster and may give better results (this would be weighted 5NN). This technique was used here: The Continuous Interpolating Self-organizing Map.
You can see and experiment with those different options here: http://www.inf.ufrgs.br/~rcpinto/itm/ (not a SOM, but a close cousin). Click "Apply" to do regression on a curve using the reconstructed vectors, then check "Draw Regression" and try the different options.
BTW, the description of your implementation is correct.
A pretty common approach nowadays is the soft subspace clustering, where feature weights are added to find the most relevant features. You can use these weights to increase performance and improve the BMU calculation with euclidean distance.
Self organizing maps are more suited for clustering(dimension reduction) rather than classification. But SOM's are used in Linear vector quantization for fine tuning. But LVQ is a supervised leaning method. So to use SOM's in LVQ, LVQ should be provided with a labelled training data set. But since SOM's only do clustering and not classification and thus cannot have labelled data how can SOM be used as an input for LVQ?
Does LVQ fine tune the clusters in SOM?
Before using in LVQ should SOM be put through another classification algorithm so that it can classify the inputs so that these labelled inputs maybe used in LVQ?
It must be clear that supervised differs from unsupervised because in the first the target values are known.
Therefore, the output of supervised models is a prediction.
Instead, the output of unsupervised models is a label for which we don't know the meaning yet. For this purpose, after clustering, it is necessary to do the profiling of each one of those new label.
Having said so, you could label the dataset using an unsupervised learning technique such as SOM. Then, you should profile each class in order to be sure to understand the meaning of each class.
At this point, you can pursue two different path depending on what is your final objective:
1. use this new variable as a way for dimensionality reduction
2. use this new dataset featured with the additional variable representing the class as a labelled data that you will try to predict using the LVQ
Hope this can be useful!
I would like to perform classification on a small data set 65x9 using some of the Machine Learning Classification Methods (SVM, Decision Trees or any other).
So, before starting with the classification I would like to do attribute analyses with PCA in Matlab or Weka (preferred MatLab). I would like to obtain which Attribute contribute most to the performance of the classifier. So I can maybe reduce the number of some Attribute or/and include more in the future. Any example of PCA can find regarding this in MatLab or Weka?
Thanks
PCA is a unsupervised feature extraction method.
If your question is on selecting attributes to use with PCA, i don't know what your purpose is but it is unnecessary to do something like that to improve classification performance. Just use the whole attributes. PCA will give you best attributes in decreasing order for each instance.
If your question is on selecting attributes after PCA, you can chose a treshold (for example 0.95) and calculate #attributes enough for treshold beginning from the first attribute to last one. You can use the eigenvalues of covariance matrix to calculate and achive treshold in PCA.
After running PCA, we know that the first attribute is the best one, the second attribute is the best one after first etc...
My task is to classify time-series data with use of MATLAB and any neural-network framework.
Describing task more specifically:
Is is a problem from computer-vision field. Is is a scene boundary detection task.
Source data are 4 arrays of neighbouring frame histogram correlations from the videoflow.
Based on this data, we have to classify this timeseries with 2 classes:
"scene break"
"no scene break"
So network input is 4 double values for each source data entry, and output is one binary value. I am going to show example of src data below:
0.997894,0.999413,0.982098,0.992164
0.998964,0.999986,0.999127,0.982068
0.993807,0.998823,0.994008,0.994299
0.225917,0.000000,0.407494,0.400424
0.881150,0.999427,0.949031,0.994918
Problem is that pattern-recogition tools from Matlab Neural Toolbox (like patternnet) threat source data like independant entrues. But I have strong belief that results will be precise only if net take decision based on the history of previous correlations.
But I also did not manage to get valid response from reccurent nets which serve time series analysis (like delaynet and narxnet).
narxnet and delaynet return lousy result and it looks like these types of networks not supposed to solve classification tasks. I am not insert any code here while it is allmost totally autogenerated with use of Matlab Neural Toolbox GUI.
I would apprecite any help. Especially, some advice which tool fits better for accomplishing my task.
I am not sure how difficult to classify this problem.
Given your sample, 4 input and 1 output feed-forward neural network is sufficient.
If you insist on using historical inputs, you simply pre-process your input d, such that
Your new input D(t) (a vector at time t) is composed of d(t) is a 1x4 vector at time t; d(t-1) is 1x4 vector at time t-1;... and d(t-k) is a 1x4 vector at time t-k.
If t-k <0, just treat it as '0'.
So you have a 1x(4(k+1)) vector as input, and 1 output.
Similar as Dan mentioned, you need to find a good k.
Speaking of the weights, I think additional pre-processing like windowing method on the input is not necessary, since neural network would be trained to assign weights to each input dimension.
It sounds a bit messy, since the neural network would consider each input dimension independently. That means you lose the information as four neighboring correlations.
One possible solution is the pre-processing extracts the neighborhood features, e.g. using mean and std as two features representative for the originals.
I need to classify pairs of image and indicate whether they're the same of not. I use several descriptors as SIFT LBP and more.
I want now to use LIBSVM to do the training and test.
how can I use teh svmTrain.
should I save only the distance between 2 descriptors and then just have 1 1:SIftDelta, 2:LBPDelta
is this the correct way or is there any better approach?
thanks
I'm not sure this is the right forum for this question, as it deals more with "high level" notions of learning, rather the specific implementation of it in Matlab.
Having said that, it seems like you are trying to combine multiple cues for learning, which is not a trivial task.
I can propose two methods for you:
Direct method - just concatenate all your descriptors into a single, very long, one and do the learning in this high dimensional space.
Do the learning in two stages (consequently, you'll have to partition your training data into two):
At the first stage, learn K classifiers, each using a different descriptor (assuming you wish to use K different descriptors).
Then, at the second stage, (using the reminder of your training data), you classify each example using the K classifiers you have: this will give you a new K-dimensional feature vector for each sample (you can put the classification result, or use the distance from the separating hyper plane to populate the k-th entry in the new descriptor). Now you can train a second classifier on the new K-dimension vectors. This second classifier gives you the final output of your multi-descriptor system.
-Enjoy!