Questions tagged [dimensionality-reduction]

Dimensionality reduction refers to techniques for reducing many variables into a smaller number while keeping as much information as possible. One prominent method is [tag pca]

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11answers
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What is dimensionality reduction? What is the difference between feature selection and extraction?

From wikipedia: 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 ...
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2answers
11k views

High-dimensional data: What are useful techniques to know?

Due to various curses of dimensionality, the accuracy and speed of many of the common predictive techniques degrade on high dimensional data. What are some of the most useful techniques/tricks/...
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6answers
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How to do SVD and PCA with big data?

I have a large set of data (about 8GB). I would like to use machine learning to analyze it. So, I think that I should use SVD then PCA to reduce the data dimensionality for efficiency. However, MATLAB ...
19
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5answers
30k views

Feature selection vs Feature extraction. Which to use when?

Feature extraction and feature selection essentially reduce the dimensionality of the data, but feature extraction also makes the data more separable, if I am right. Which technique would be ...
8
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3answers
2k views

What does the long curve-shape t-SNE mean?

I use 1-D CNN input 1*512 size time series data which randomly fragment segment, the output will classify input into 10 classes. After training the CNN, I apply t-SNE to the prediction which I fed in ...
4
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1answer
235 views

What is major difference between different dimensionality reduction algorithms? [closed]

I find many algorithms are used for dimensionality reduction. The more commonly used ones (e.g. on this page ) are: ...
27
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8answers
3k views

Purpose of visualizing high dimensional data?

There are many techniques for visualizing high dimension datasets, such as T-SNE, isomap, PCA, supervised PCA, etc. And we go through the motions of projecting the data down to a 2D or 3D space, so we ...
20
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3answers
7k views

Nearest neighbors search for very high dimensional data

I have a big sparse matrix of users and items they like (in the order of 1M users and 100K items, with a very low level of sparsity). I'm exploring ways in which I could perform kNN search on it. ...
18
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1answer
2k views

Are t-sne dimensions meaningful?

Are there any meanings for the dimensions of a t-sne embedding? Like with PCA we have this sense of linearly transformed variance maximizations but for t-sne is there intuition besides just the space ...
16
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3answers
8k views

Why are autoencoders for dimension reduction symmetrical?

I'm not an expert in autoencoders or neural networks by any means, so forgive me if this is a silly question. For the purpose of dimension reduction or visualizing clusters in high dimensional data, ...
2
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1answer
7k views

What is a good explanation of Non Negative Matrix Factorization?

I am trying to find a resource to understand non-negative matrix factorization. Apart from Wikipedia, I couldn't find anything useful.
25
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5answers
16k views

Improve the speed of t-sne implementation in python for huge data

I would like to do dimensionality reduction on nearly 1 million vectors each with 200 dimensions(doc2vec). I am using TSNE ...
15
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1answer
3k views

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 ...
6
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1answer
2k views

What does it mean by “t-SNE retains the structure of the data”?

I was learning about t-SNE when I was told that t-SNE retains the structure of the data in the embeddings. What exactly does this mean ? How does the algorithm achieve this ? So far I have ...
9
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1answer
2k views

t-SNE: Why equal data values are visually not close?

I have 200 data points that have the same values on all features. After t-SNE dimension reduction they doesn't look so equal anymore, just like this: Why aren't they on the same point in the ...
4
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2answers
4k views

machine learning algorithms for 2d data?

I'm looking for a supervised learning algorithm that can take 2d data for input and output. As an example of something similar to my data, consider a black image with some sparse white dots. Blur that ...
4
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4answers
9k views

Can I use unsupervised learning followed by supervised learning?

I have a question about classifying documents using supervised learning and unsupervised learning. For example: - I have a bunch of documents talking about football. As we know, football has a ...
3
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1answer
429 views

Online/incremental unsupervised dimensionality reduction for use with classification for event prediction

Consider the application: We have a set of users and items. Users can perform different action types (think browsing, clicking, upvoting etc.) on different items. Users and items accumulate a "...
4
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2answers
356 views

Elimination of features based on high covariance without affecting performance?

I ran into a question where the answer ran me into a big doubt. Suppose we have a dataset $A=${$x1,x2,y$} in which $x1$ and $x2$ are our features and $y$ is the label. Also, suppose that the ...
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3answers
1k views

PCA on Neural Networks dimensions reduction? [closed]

The dataset which was extracted from the database consists of more than 50 columns, I call these columns dimensions, can I call them dimensions? Obviously, I have to do dimension reduction on ...