All Questions
Tagged with autoencoderdimensionality-reduction
14 questions
0votes
0answers
21views
About autoencoder's latent state regularity
Suppose we are dealing with the problem of dimensionality reduction of an input $\mathbf{x}\in\mathbb{R}^N$, by employing an autoencoder, as a composition of the encoder and decoder map $\mathbf{x} \...
0votes
0answers
125views
Is it possible to use text Auto Encoders without text generation?
I have a use case where I have large texts, and a lot of it. Pretty often the sequence length exceeds 1000 tokens. I need a lower dimensional compression of the texts as an input for a classifier. The ...
- 101
0votes
1answer
153views
An autoencoder setup for anomaly detection
I am doing anomaly detection using machine learning. i have tried different models like isolation forest, SVM and KNN. The maximum accuracy that I can get from each of them is $80\%$ accordind to my ...
- 11
1vote
1answer
118views
Can we use feature selection and dimensionality reduction together?
I have a dataset having about 10,000s of features. The features have a hierarchy inherent to them. I found an algorithm performing feature engineering, taking the hierarchy of the features into ...
- 23
1vote
0answers
26views
Do all autoencoders perform dimensionality reduction [closed]
I want to use Convolutional autoencoder to find patterns in data as well as reduce dimensions. Can it be used for this purpose? Moreover, is removal of multicollinear features through autoencoder ...
1vote
0answers
292views
Autoencoder for Dimensionality Reduction - varying result - parameter tuning
I'm not an expert in autoencoders or neural networks by any means, so forgive me if this is a silly question. The problem and steps taken to solve problem are as follows: There exists a data set with ...
- 11
1vote
0answers
51views
Analogy between Autoencoder and PCA
I know that Autoencoders can be regarded as non-linear generalisations of PCA, but I struggle to understand in depth the analogy between the two. Once PCA has been performed on a function $F(\vec{\...
1vote
1answer
790views
Steps on how to use autoencoders to reduce dimensions
I have a dataset that contains text columns. I have used tf-idf to convert those text columns to numerical columns. I want to reduce the dimension of the dataset since tf-idf creates a multitude of ...
2votes
1answer
1kviews
Keras - Autoencoder different from Encoder + Decoder
I build a CNN 1d Autoencoder in Keras, following the advice in this SO question, where Encoder and Decoder are separated. My goal is to re-use the decoder, once the Autoencoder has been trained. The ...
- 21
1vote
1answer
959views
Autoencoder or layer-based dimensionality reduction?
I have a few TB of wide data. I want to reduce the number of features in my dataset before feeding my dataset into a classification model... or should I not? Obviously, I will want to try both ...
- 549
1vote
2answers
1kviews
Which algorithm can be used to reduce dimension of multiple time series?
In my dataset, a data point is essentially a Time series of 6 feature over a year per month so in all, it results in 6*12=72 features. I need to find class outliers so I perform dimensionality ...
- 235
0votes
1answer
308views
Does it make sense to train an Autoencoder for Dimensionality Reduction using Mini-Batch Gradient Descent?
I want to reduce the dimensionality of a dataset using a stacked Autoencoder. The size of the dataset and the computing power at my disposal make it very difficult to train the Network using simple, ...
- 6,435
1vote
1answer
2kviews
How to calculate compression ratio when using autoencoder in neural network
For example, if I use an autoencoder to compress a 1000 dimensional data set to 25 dimensions. Is the compression ratio is 40:1? Other info: The dataset contains 5000 samples. 2 million parameters ...
20votes
3answers
11kviews
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, ...
- 261
