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The first eigenvalue in your plot is non-zero, whereas the last is one.
This seems to suggest that the Laplacian whose eigenvalues you are computing is different from the standard Laplacian $ L = D - W$, where $D$ is the matrix whose diagonal entries are degrees of nodes and $W$ is the weighted adjacency matrix.
So in your plot, $\lambda_{19} = 1$ and $\...
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Let's say that your dataset is a bunch of temperatures with a bunch of features related to when/where the measurements were made.
A measurement is a global outlier if it diverges from the distribution of temperatures regardless of the features (when and where) because that measurement is far off the global distribution (100°C for example).
The example makes ...
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I strongly recommend to start with a simple statistical data analysis. In this approach you take the Moving Average/Median of signals and if one signal shows a magnitude -/+ 3 times standard deviation, you mark it as anomaly. Please have a look at this answer for Python code.
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Welcome to the community!
You may want to refer to a tutorial on Agglomerative Hierarchical Clustering before reading this answer. My explanation is more practical.
Assume the data below:
from scipy.cluster.hierarchy import ward, fcluster
from scipy.spatial.distance import pdist
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.text import ...
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The limited size is not especially an issue. However the normal way to do that is to use a supervised classification method (for instance decision trees, but there are many options), and this means training a model from a large enough set of annotated instances.
If obtaining a training set is not possible, you could try some kind of similarity-based approach ...
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The Jaccard coefficient (or Jaccard similarity) is defined on two sets $A$ and $B$:
$$
J(A,B) = {{|A \cap B|}\over{|A \cup B|}} = {{|A \cap B|}\over{|A| + |B| - |A \cap B|}}
$$
There is a single definition for this coefficient, but note that Jaccard is a general similarity measure, it is not specifically designed as an evaluation measure. So assuming one ...
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I agree with your assumption, the vector space is the same so I don't see any major problem with this approach.
Still this approach might cause some more subtle bias, depending on the differences between the models (sets of terms, number of clusters). I could imagine the following problems happening:
if there is a big difference in number of clusters ...
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Your task is achieved by Subspace Clustering
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In addition to the answers provided, you can:
1.) Train jointly a CNN (or Autoencoder) with clustering on your data. (DCN, kmeansNet,..)
2.) Pretrain a CNN using self-supervision on your data. (Have a look into the vast self-supervision literature, e.g. this work).
3.) Use an alternating scheme to train a CNN classifier on soft-labels provided by a ...
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