Questions tagged [manifold]
10 questions
15
votes
1 answer
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 closer in a 2D t-sne visualization can be assumed…
Javierfdr
- 1,500
- 13
- 14
4
votes
1 answer
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" means that the non-linear dimensionality…
Javierfdr
- 1,500
- 13
- 14
3
votes
1 answer
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".
Reference: Section 4.3 of the paper Generative Adversarial Text…
Arpit Gupta
- 33
- 2
3
votes
1 answer
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 predicts arbitrary points in the n-dimensional real…
JoelKuiper
- 191
- 1
- 7
2
votes
0 answers
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 imagine that my data points naturally sit on some…
user
- 31
- 1
1
vote
0 answers
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 to determine the dimensionality of the…
HdeV
- 11
- 1
1
vote
0 answers
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. measurements of a chemical physical attributes to…
Ghostintheshell
- 451
- 1
- 5
- 7
1
vote
2 answers
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 to project node2vec embeddings to a slightly…
Sijie Chen
- 11
- 2
0
votes
1 answer
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-dimensional space, unlike other manifold-learning…
bkoodaa
- 323
- 3
- 5
- 8
0
votes
1 answer
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 told that I should be using these methods to…
Finncent Price
- 111
- 4