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I made a model for clustering and it's encoded dimension is about 3000. To check if the autoencoder is well established, I draw a 2d_pca plot and 3d_pca and the plots look nice.
My question is that, what is general way to cluster with this encoded features?
I think about some options:
First: to use all encoded features.
Second: to use all encoded pca features.
Third: to use some encoded pca features explaining almost 70% variance.
I think usual papers don't refer to it.
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I am working on a small computer vision project and I'm using convolutional nets for classification. I have already used dropout, l1, l2 regularization and data augmentation to reduce overfitting. Are there any other techniques and algorithms for improving model accuracy and reducing overfitting?
there could be a 100 solutions
Use pretrained model (transfer learning).
Try to implement a smaller network.
Bigger data set.
Try different parameters[learning rate, batch size..].
Use grid search for these parameters.
Try data augmentation for your training data set.
...
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If i have data in NxN grid format (for example see figure) and each cell size is given by (Xmax/N) x (Ymax/N) and data given in each cell is the frequency data of that cell. What is the best way to graphically represent this data in MATLAB such that it is easy to view the frequency for each cell? If I would like to make it like in this example (see colormap), how can I do that what function should I used?
Your choice. Here I put several possibilities:
bar3: if you the points are discrete by meaning
surf or mesh : if the points are continuous by meaning
-imshow or image
in MATLAB 2017b or newer, heatmap
-contour, if you have a sufficiently detailed data
There may be more, please feel free to add them to the post.
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Im still not sure how clustering can be used for predictive analytics?
can someone tell me how to predict the future from extracting clusters?
generally, clustering isn't used for prediction but for labeling or analyzing existing set of data points.
after you use clusters to label your data points and divide them into groups based on common traits, you can run other prediction algorithms on that labeled data to get predictions.
I don't think clustering leads directly to predictions, other than cases of clusters that are well separated and can be used to make inferences about the data points and the properties of the clusters
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I have plotted the standard deviation of different regions.Can anyone help me to get the conclusions from this boxplot. I just want to conclude the properties of regions. In this figure, eigth object is odd one. What is the significance of whiskers?
How to change the xlabel as region1 ,region2 etc
Coclusions: wide part of your data does not follow a normal distribution. You need something like Violin Plots to see what is rally happening in your data.
Specially for 3-7, as it seems that the number of the outliers is too big.
But remember: Conclusions are obtained from data, not from the plotting option you chose for your data!
about changing the xlabel.... have you tried the function xlabel....?
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I need to design a neural network which has the following behavior:
p(1)={0,1,1,1} outputs a(1)={0,1,0,0}
p(2)={1,1,0,1} outputs a(2)={0,0,1,0}
p(3)={0,0,1,0} outputs a(3)={0,0,0,1}
p(4)={0,0,1,1} outputs a(4)={1,1,0,1}
How can i do so? Which type of neural network should I use? Which learning method can be used here?
Thanks.
At first glance it seems as though you could use a simple feedforward neural network with one input layer one, one hidden layer and one output layer. You can use your training data to train the neural network using the backpropogation algorithm.
See this page for more details:
http://en.wikipedia.org/wiki/Backpropagation