Kernel functions are a class of functions which transform the original data into a new space in which the classes of the data are easier to separate by a kernel algorithm.
Questions tagged [kernel]
86 questions
26
votes
1 answer
back propagation in CNN
I have the following CNN:
I start with an input image of size 5x5
Then I apply convolution using 2x2 kernel and stride = 1, that produces feature map of size 4x4.
Then I apply 2x2 max-pooling with stride = 2, that reduces feature map to size 2x2.…
koryakinp
- 436
- 1
- 5
- 14
12
votes
1 answer
What are practical differences between kernel k-means and spectral clustering?
I've been lately wondering about kernel k-means and spectral clustering algorithms and their differences.
I know that spectral clustering is a more broad term and different settings can affect the way it works, but one popular variant is using…
Kuba_
- 264
- 1
- 10
7
votes
1 answer
How do I interpret the length-scale parameter of the RBF kernel?
According to the Scikit-Learn documentation for the RBF kernel:
The length scale of the kernel. If a float, an isotropic kernel is used. If an array, an anisotropic kernel is used where each dimension of l defines the length-scale of the respective…
JGG
- 503
- 1
- 5
- 7
7
votes
3 answers
Kerns LSTM kernel
I am trying to understand how the weight matrix in an LSTM cell is used. An LSTM unit has several weight matrix: Wf, Wi, Wc, Wo like below:
( from http://colah.github.io/posts/2015-08-Understanding-LSTMs/ )
At the same time, I am playing with the…
Edamame
- 2,705
- 5
- 23
- 32
6
votes
3 answers
Why spectral clustering results in disjointed cluster?
I'm working on a project where I have to dynamically cluster the position of objects with respect to one coordinate. So I'm essentially dealing with subsequent frames and each frame represents a one-dimensional dataset. The intuition behind…
Kuba_
- 264
- 1
- 10
6
votes
1 answer
Does increasing kernel size in a CNN result in higher accuracy on the training set?
In a convolutional neural network, does increasing the size of kernel always result in better training set accuracy? For example, if I use 5x5 kernels in a CNN instead of 3x3 ones, will it always generate better training accuracy?
Increasing kernel…
Saptarshi Roy
- 427
- 2
- 4
- 11
6
votes
1 answer
Is there any conceptual relationship between 'kernel' in SVM and 'kernel' in convolution neural net?
In SVM, we have kernel function that maps an input raw data space into a higher dimensional feature space
In CNN, we also have a 'kernel' mask that travels the input raw data space (image as a matrix) and map it to another space.
Given the fact that…
eight3
- 161
- 1
5
votes
1 answer
Theoretical differences between KPCA and t-SNE?
I (think I) understand the underlying principles of most dimensionality reduction methods (MDS, IsoMap, t-SNE, Spectral Embedding, Diffusion maps, etc...).
Some of the algorithms I use the most are Kernel PCA (with a gaussian kernel) and t-SNE. My…
Rayamon
- 153
- 3
4
votes
1 answer
Has anyone succeeded in finding a good Scala/Spark kernel for Jupyter?
The ones I've tried so far
Almond: Works very well for just Scala, but you have to import dependencies, and it gets tedious after a while. And unfortunately can't run when using Spark with YARN instead of Local.
Spylon-kernel: Kernels connects, but…
Varun Gawande
- 141
- 3
4
votes
1 answer
Designing Custom Kernel from my Mathematical model
I derived a mathematical model for a porous system and the final function looks like this ,
after going through the Mercers Theorem and its condition for a kernel I would love to write an SVM kernel with this equation on python and use it for…
Raymond Confidence
- 41
- 1
3
votes
1 answer
Why are neural networks equivalent to kernel methods?
I read a recent paper by Pedro Domingos, claiming that Every Model Learned by Gradient Descent Is Approximately a Kernel Machine.
I wanted to understand the key idea a little better. Why are neural networks equivalent to kernel methods? And in…
robbmorganf
- 31
- 1
3
votes
1 answer
What is a good method for detecting local minims and maxims?
I'm using kernel density estimation in order to compute
probability density function for item occurrence.
Using this output, i want to find all the local minims and maxims.
I'm interested in different methods for local-min/max extraction.
cristian hantig
- 131
- 3
3
votes
1 answer
Does it matter whether we put regularization parameter ($C$) with error or weight term in Kernel ridge regression?
Kernel ridge regression associate a regularization parameter $C$ with weight term ($\beta$):
$\text{Minimize}: {KRR}=C\frac{1}{2} \left \|\beta\right\|^{2} + \frac{1}{2}\sum_{i=1}^{\mathcal{N}}\left\|e_i \right \|_2^{2} \\
\text{Subject to}:\…
Chandan Gautam
- 301
- 2
- 13
3
votes
3 answers
How to understand features impact in a non linear case?
I give a simple example:
I have a set of houses with different features (# rooms, perimeter, # neighbours, etc...), almost 15, and a price value for each house. The features are also quite correlated (i.e. perimeter is often correlated with…
A M
- 61
- 4
3
votes
3 answers
Intuition behind the fact that SVM uses only measure of similarity between examples for classification
I have read about SVM and although I did not understand the math behind it completly, I know that it produces decision plane with maximum margin between examples of different classes and role of support vectors in the process.
I also know that SVM…
saurabh
- 31
- 2