In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction.
Questions tagged [dimensionality-reduction]
422 questions
2
votes
1 answer
Accessing reduced dimensionality of trained autoencoder
Here is a autoencoder trained on mnist using PyTorch :
import torch
import torchvision
import torch.nn as nn
from torch.autograd import Variable
cuda = torch.cuda.is_available() # True if cuda is available, False otherwise
FloatTensor =…
blue-sky
- 51,962
- 152
- 427
- 752
2
votes
1 answer
Project vector w onto vector v and draw perpendicular line - preparation for PCA
I want to do vector projection as preparation for PCA where I followed This tutorial for the calculation of the vector projection.
w is the vector which 'points' onto the data points, v is the vector which spans the line onto which w should be…
2Obe
- 3,570
- 6
- 30
- 54
2
votes
1 answer
Filter DataFrame after sklearn.feature_selection
I reduce dimensionality of a dataset (pandas DataFrame).
X = df.as_matrix()
sel = VarianceThreshold(threshold=0.1)
X_r = sel.fit_transform(X)
then I wanto to get back the reduced DataFrame (i.e. keep only ok columns)
I found only this ugly way to…
Intelligent-Infrastructure
- 1,188
- 4
- 18
- 39
2
votes
1 answer
what is the tsne initial pca step doing
Looking at the parameters to the Rtsne function:
https://cran.r-project.org/web/packages/Rtsne/Rtsne.pdf
There is a parameter called "pca" defined as "logical; Whether an initial PCA step should be performed (default: TRUE)"
Let's say you have a 10…
user3022875
- 8,598
- 26
- 103
- 167
2
votes
1 answer
Subset variables by significant P value
I'm trying to subset variables by significant P-values, and I attempted with the following code, but it only selects all variables instead of selecting by condition. Could anyone help me to correct the problem?
myvars <-…
lydias
- 841
- 1
- 14
- 32
2
votes
3 answers
Convolution to reduce dimensionality of one dimensional vector
In a CNN, if the output is a one dimensional vector(say, a pre-logit layer), how would one reduce the dimensionality down to a specified size, using only convolutions?
How does one derive the filter dimensions/receptive field to accomplish such a…
Jack H
- 2,440
- 4
- 40
- 63
2
votes
1 answer
t-SNE generates different results on different machines
I have around 3000 datapoints in 100D that I project to 2D with t-SNE. Each datapoint belongs to one of three classes. However, when I run the script on two separate computers I keep getting inconsistent results. Some inconsistency is expected as I…
perfall
- 118
- 1
- 9
2
votes
2 answers
Is t-SNE's computational bottleneck its memory complexity?
I've been exploring different dimensionality reduction algorithms, specifically PCA and T-SNE. I'm taking a small subset of the MNIST dataset (with ~780 dimensions) and attempting to reduce the raw down to three dimensions to visualize as a scatter…
Yu Chen
- 6,540
- 6
- 51
- 86
2
votes
1 answer
Simple correspondence analysis in Python
How can I run simple correspondence analysis (CA) in Python? In the sklearn library, there only appears to be multiple correspondence analysis (MCA) and canonical correspondence analysis (CCA) options. However, my data is not categorical and does…
FFT
- 929
- 8
- 17
2
votes
3 answers
How to vectorize populating larger matrix with items of smaller matrix in python
I have some small symmetric matrices that are low dimensional representations of larger symmetric matrices. I have a vector that is a key showing which cells of the high-D matrix should be linked to which cells in the low-D matrix.
I would like to…
Aki Nikolaidis
- 31
- 7
2
votes
2 answers
Multiple Correspondence Analysis with None-Binary Categorical Dummy Variables in Python using mca and prince modules
I am using a python package for multiple correspondence analysis on multiple categorical variable. I am studying a set geological data, here is a sample preview:
Quartz Oxides Hematite Limonite Geothite Clay Soil_Type
1 …
Gerard
- 518
- 4
- 19
2
votes
0 answers
TSNE (sklearn.manifold) KL divergence error
I'm trying to visualise my sample data using tsne. The code seems working fine but I am not sure about the correctness of my output.
tsne = manifold.TSNE(n_components = 2,
random_state = 0,
verbose = 5,
…
Roy
- 45
- 7
2
votes
1 answer
How to plot classification regions in a lower dimensional space?
I'm working in a space which has 8 dimensions (i.e. 8 features). I have plotted the data points in 2D by applying PCA as well as TSNE. Now I would like also to draw the borderlines of the classifiers I use as shown here. By the way, I'm using…
machinery
- 5,972
- 12
- 67
- 118
2
votes
1 answer
Tensorflow reduce dimensions of rank 3 tensor
I am trying to build a CLDNN that is researched in the paper here
After the convolutional layers, the features go through a dim-reduction layer. At the point when the features leave the conv layers, the dimensions are [?, N, M]. N represents the…
unknown_jy
- 757
- 9
- 19
2
votes
1 answer
Should I perform Linear Discriminant Analysis over the entire dataset for dimensionality reduction?
I don't understand how to use LDA just for dimensionality reduction.
I have a 75x65 matrix with 64 features and 1 column for the class index. This matrix can be found here.
I am trying to use LDA for dimensionality reduction, using this function…
