Refers to techniques for reducing a large number of variables or dimensions spanned by data to a smaller number of dimensions while preserving as much information about the data as possible. Prominent methods include PCA, MDS, Isomap, etc. The two main subclasses of techniques: feature extraction ...
0
votes
0answers
13 views
Doubt regarding PCA
I have 5 different independent variables, lets name 1 to 5. The 3rd IV has 10 sub-variables under it and 4th IV has 11 sub-variables in it. Whereas other 3 IV's have just two sub-variables (...
0
votes
0answers
20 views
Principal components: Can I interpret PCA as essentially a change of basis
I was hoping that someone could simply validate or correct my interpretation of Principal Components Analysis. There are a lot of questions on this site about Principal Components analysis--some ...
0
votes
0answers
5 views
How to cluster and reduce dimensionality of Barthel scale data
I 'd like to cluster subjects with their Barthel scale. It looks like below:
Since the Barthel scale data is an ordinal scale and a likert-type rating data (given number of points assigned to each ...
0
votes
2answers
44 views
How to find the minimum set of vectors?
I have a set of vectors. For simplicity, suppose there are $n=20$ vectors and each has $p=5$ elements.
These vectors are generated from some experiments, and so it is not very apparent how they are ...
-2
votes
1answer
28 views
Example for Principal Component Analysis
Where principal component analysis can potentially be used ?
some examples with some explanation would be great
0
votes
1answer
26 views
What does ICA return?
I am confused with ICA. With PCA I understood that it always gives the components with maximum variance. What does ICA return? Does it return components with maximum independence? How to find best ...
0
votes
1answer
12 views
Self-organizing map dimension
I just have a question on the chosen dimension of Self-organizing map.
Typically, an SOM has a dimension of 2 or 3 but rarely larger than that. Is there a particular reason why this is the case?
0
votes
0answers
11 views
Gaussianity and Whitening in ICA - The feeling and intuition behind it
I understand what ICA does at a high level but in the cocktail party problem context. All the examples, articles I have read take a similar problem to explain ICA where the aim is to derive the ...
0
votes
0answers
6 views
optimality of LDA dimensionality reduction
typically when you do dimensionality reduction using LDA, you select $n_{class}-1$ vectors with largest eigenvalues as discriminants given the fact that you only need $n-1$ dimensions to classify $n$ ...
1
vote
1answer
98 views
How do I get the density of a region in a vector space?
I have a simple problem, which I think must have an easy solution.
I have a vector space say with a 1000 dimensions for each vector.
Now, I have a large number of sample vectors from this vector ...
0
votes
0answers
21 views
Dimensionality reduction for multivariate time series data?
I'm trying to perform dimensionality reduction on a multivariate time-series data set which gives information on the medical history of a group of 12000 patients. The data contains 19 different ...
1
vote
0answers
25 views
Linear Discriminant Analysis as Dimensionality Reduction very sensitive to Training Set size
I'm working with supervised classification of object-based satellite imageryand currently investigate different dimensionality reduction methods on their suitability to this application. As part of my ...
1
vote
0answers
26 views
What kind of PCA is best for my data?
I'm using sklearn to reduce the dimensionality of an object.This scan is 3D, with shape [15,15,15] and contains mostly zeros, altough the non-zero values are usally clustered together in some groups.
...
0
votes
1answer
27 views
PCA Dimension Reduction for Cox Proportional Hazard Model
I am trying to figure out if it is possible and appropriate to use PCA to reduce the dimensions of my dataset? A little background - I have survival data with 8 covariates. I have run a Pearson's ...
0
votes
0answers
19 views
Variability in categorical variables while reducing dimensionality
If a dataset has a variable with categorical values (A, B, C, D, E, F), would converting them to numeric and breaking them down into separate columns (for example, column A that will have 1s for all ...
2
votes
0answers
24 views
PCA: mean of marginal distribution of high-dimensional vector
Consider the following probabilistic model:
$$p(x) = \mathcal{N}(0, I_d), \ x \in \mathbb{R}^d$$
$$p(y|x) = \mathcal{N}(Wx + \mu, \sigma^2I_D),
\ y, \mu \in \mathbb{R}^D, W \in \mathbb{R}^{D\times d}...
0
votes
0answers
15 views
Performing PCA on properly scaled data [duplicate]
I would like to perform PCA on a dataset, however, not all of the data is in the same scale. Some variables are Height, Weight, Age, while others are Dribbles per Game, Shots per Game, Blocks per Game....
0
votes
1answer
23 views
Non-negative matrix factorization (NMF) on mixed data using 1-hot encoding
From a standpoint of interpretation, can I use NMF on one-hot encoded categorical data for dimension reduction? I have mixed data and was thinking about one-hot encoding the categorical features and ...
0
votes
0answers
11 views
How do I decide number of hidden layers and neurons for Auto-encoder for dimension reduction?
Supposed I have a 30-dimensional problem and would like to reduce it into 3-dimensional latent space. How do I decide which architecture will be better for my problem?
This might be a silly question ...
3
votes
2answers
218 views
Why with two classes, LDA gives only one dimension?
I am working with dimensionality reduction algorithms. Linear Discriminant Analysis (LDA) is a supervised algorithm that takes into account the class label (which is not the case of PCA for example). ...
1
vote
0answers
35 views
Comparing PCA representations between low-pass and high-pass filtered time-series data
I am currently trying to reduce the number of variables I input into a vector autoregressive (VAR) model. For those that don't already know, VAR models are used on time-series data. My primary concern ...
1
vote
0answers
13 views
Bag of Visual Words: is feature extraction even needed?
I'm currently implementing a BoVW as part of my lab project. The steps the algorithm used are as follows:
spliting all photos into patches
cluster these pathces using K-means based on pixel values of ...
1
vote
1answer
35 views
Regression with multidimensional output variable Y
Say we have an $N \times q$ matrix $Y$ with $N>q$.
Also, we have an $N \times p$ data matrix $X$.
We are interested in a model of $Y = X \times W + \epsilon$, where $W$ is a $p \times q$ matrix ...
0
votes
1answer
20 views
Clustering by same random projection
I have $N$, $1024$-dimensional vectors. I want to cluster them by some similarity. Given the high dimensionality, standard metrics won't work. I tried a few Approximate Nearest Neighbor ...
1
vote
1answer
72 views
Can I reconstruct the original data from a dimensionality reduced data using t-SNE?
I have implemented dimensionality reduction using PCA, AE, and t-SNE! I am trying to compare the output of the three methods by finding the reconstruction error. I have managed to reconstruct the data ...
3
votes
1answer
57 views
Dimensionality reduction: Feature Selection or Feature Extraction?
Case:
I have a dataset with a large number of features, which I want to reduce. Should I look for a method that identifies the most important ones and throw away the rest, or should I look for a ...
1
vote
2answers
38 views
What exactly is called “embedding” in dimensionality reduction?
In the following slide I do not understand the definition of the term embedding.
In the third paragraph, it says it is a mapping from low-dim. to high-dim, but in the last paragraph it suggests that ...
0
votes
0answers
12 views
Dimensionality Reduction/Clustering - Unordered Attribute Groupings
I'm looking to do some dimensionality reduction on data where each record describes a common set of $k$ attributes on $n_i$ different subjects (in my case, members of a household - think age, gender, ...
0
votes
0answers
25 views
images in the paper “Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs”
I have some confusion about this image in the paper "Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs"
I am wondering what kind of technique they use to lower the dimension of the ...
0
votes
0answers
80 views
Using a single principle component (PC) space to describe how a dataset changes across conditions
Given a design matrix that consists of N (>100) variables and J (>100) observations (the data, itself, is actual time-series):
...
2
votes
0answers
153 views
Is MCA equivalent to PCA when all variables are binary?
I am looking to apply principal component analysis on binary (true/false) data, and I have come across the "equivalence between PCA and MCA" (Multiple Correspondense Analysis) for binary data, but ...
-1
votes
1answer
48 views
Dimension reduction [closed]
Can i apply dimension reduction method such as random forest, lasso, factor anaysis or principle compoenet analysis on data which was extracted from two stage stratfied survey
1
vote
0answers
15 views
Recognition accuracy decreases as the number of LDA features increase?
Helloy guys. I'm currently using KALDI building an HMM-GMM system and I added LDA transform to the original 12-dim features, wondering if I can get a better performance of the system.
The input dim ...
0
votes
0answers
62 views
LDA vs PCA 2d plot
PCA (Principal Component Analysis) is often used to represent 2d or 3d plot of the data, where y=PC2 and x=PC1 (eventually z=PC3).
Given that there is an 'order' between components, it makes sense to ...
0
votes
0answers
48 views
PCA on layers of deep neural network in time series prediction (or linear regression in general) for explainability
Are there any references on applying PCA or some other dimensionality reduction to the intermediate layers of deep neural networks in the context of time series or regression? I was thinking that ...
0
votes
0answers
20 views
MCA and K-Means simultaneously
I was reading the article Multiple Correspondence K-means: Simultaneous versus sequential approach for dimension reduction https://link.springer.com/chapter/10.1007/978-3-319-55477-8_8
I have this ...
1
vote
2answers
64 views
How to plot High Dimensional supervised K-means on a 2D plot chart
I'm Having a ML problem where my data set contains 80 features labelled into 3 groups (0, 1, -1).
I want to plot the data on a 2D surface to see how "close" (similar) data with ...
2
votes
1answer
21 views
Framework for reducing the dimension of the features of multiple correlated Time Series with a notable amount of memory
I have a dataset that I am trying to analyse that consists of multiple (~500) time series each with around 25 observations. For each observation I have a large number of covariates, some of which will ...
0
votes
1answer
46 views
Dimensionality Reduction on VGG Image Vector
I have a random forest model which I am using to make retail demand predictions. I am looking at trying to leverage product image data to improve the predictions and have put the images through VGG-...
1
vote
1answer
158 views
Principal component analysis and DBScan
My data has 30 dimensions and 150 observations.
I want to cluster the data with DBScan.
Is there a difference between:
1. Performing a PCA and clustering all 30 principal components or
2. Just ...
1
vote
1answer
38 views
Autoencoders and/or PCA for highly sparse float vectors and a dataset of more than 2 million examples
I have a highly dimensional sparse dataset composed of 2.5 million of examples as follow :
dataset_dimension=[2500000,360,280,18]
Each example of this dataset ...
1
vote
0answers
28 views
Obtaining hard, overlapping clusters using non-negative matrix factorization
From my understanding non-negative matrix factorization (NMF) provides a natural way to obtain soft clusters from a non-negative $n$x$m$ data matrix $X$. NMF decomposes $X$ into two non-negative ...
0
votes
1answer
18 views
Flag redundant categorical variables in a big dataset
I have a dataset with ~150 categorical variables and ~150k rows. It is expected beforehand that a number of the categorical variables will be either identical, or nearly so. I would like to code ...
4
votes
2answers
354 views
Issue with the results of PCA component values
I am performing PCA on a dataset of (28 features + 1 class label) and 11M rows (samples) using the following simple code:
...
0
votes
1answer
53 views
Data reduction with nominal variable data
I have a bunch of factor variables. I believe the data comes from only a few clusters. I'd like to analyze the data and perform data reduction. I want to know the ...
1
vote
0answers
24 views
PCA and data normalization
I have a database where the number of features is about 60 times greater than the number of samples (60000 to 1000). I want to use some dimensional reduction techniques, including PCA.
Data are all ...
2
votes
3answers
281 views
Are dimensionality reduction techniques useful in deep learning
I have been working on Machine learning and noticed that most of the time, dimensionality reduction techniques like PCA and t-SNE are used in machine learning. But, i rarely noticed anyone doing it ...
1
vote
1answer
39 views
H2O PCA number of components
I wonder why number of components in H2o PCA algorithm is limited to 9. It is not sure sometimes to be enough.
k: Specify the rank of matrix approximation. This can be a value from 1 to 9 and ...
1
vote
0answers
29 views
Interpreting PCA, comparing genetic structure within mice population from two continents
I'm interested in comparing the intra-regional genetic relatedness between two populations of mice. One population is native to Europe, while the other is native to North America.
I ran PCA to find ...
0
votes
0answers
36 views
PCA: Are the first two components enough to find which two data sets are more tightly clustered?
I will create an example to explain my question. Say I have two independent data clusters in a 3D space (x,y,z). The two data clusters, Cluster A and B, are very distant to each other, such that ...
