Questions tagged [manifold]
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11
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Examples of distance "hyperparameters" used in clustering
From what I've seen in clustering, distance is taken as a hyper parameter (which is to be selected) when inferring the relationships/clusters between points.
What are some examples of highly-cited ...
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How do I interpret low dimentional embeddings of high dimentional embeddings?
I am trying to understand what I am supposed to learn about a problem when using dimensionality reduction methods. In particular, I am referring to methods like t-SNE and UMAP.
For the most part I am ...
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When visualizing graph nodes, should I use apply PCA to node2vec embedding?
I am trying to visualize graph nodes using node2vec embedding.
The node2vec embeddings has lengths of 50~100 dimensions.
I have two plans:
use umap to project node2vec embeddings to 2D space
use PCA ...
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Can an Isomap be embedded in a manifold of higher dimension than the corresponding MDS?
I am using the Isomap algorithm to operate a dimension reduction on a distance matrix $M_{dist}$.
For a given choice of nearest neighbors k to compute the geodesic distance, I use the following method ...
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Generative Adversarial Text to Image Synthesis
Can anyone explain the meaning of this line:
"Deep networks have been shown to learn representations
in which interpolations between embedding pairs tend to
be near the data manifold".
...
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Hyperbolic coordinates (Poincaré embeddings) as the output of a neural network
I'm trying to build a Deep Learning predictor that takes as the input a set of word vectors (in Euclidian space) and outputs Poincaré embeddings. So far I am not having much luck, because model ...
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Dimension of the manifold on which my data sits
Suppose that I have data points, in the form of vectors with binary entries. We create a metric space, or Vietoris-Rips complex, using the Hamming distance between the data points.
I would like to ...
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Can I use manifold learning to transform the feature set as a substitute of graph kernel of SVC
I just wonder since the manifold learning under scikit-learn has component of graph-based transformation (e.g. Shortest-path graph search under Isomap) I can then transform the feature data set (i.e. ...
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Difference between MDS and other manifold learning algorithms
From sklearn docs:
Note that the purpose of the MDS is to find a low-dimensional representation of the data (here 2D) in which the distances respect well the distances in the original high-...
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Can I apply Clustering algorithms to the result of Manifold Visualization Methods?
Some methods related to manifold-learning are commonly stated as good-for-visualization, such as T-SNE and self-organizing-maps (SOM).
I understand that when referring specifically to "visualization" ...
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Can closer points be considered more similar in T-SNE visualization?
I understand from Hinton's paper that T-SNE does a good job in keeping local similarities and a decent job in preserving global structure (clusterization).
However I'm not clear if points appearing ...
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