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Questions tagged [dimensionality-reduction]

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.

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Getting dimensions wrong when creating a feed-forward auto-encoder in Theano/Lasagne

I want to create a simple autoencoder with 3000 input, 2 hidden and 3000 output neurons: def build_autoencoder(input_var=None): l_in = InputLayer(shape=(None,3000), input_var=input_var) l_hid = DenseLayer( l_in, num_units=2, …
Romeo Kienzler
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SVD for String values

I want to perform singular value decomposition on a large event data. Lets say I have 300+ attributes. And atlas 50% of them are string values. Like a city name and other possible string values. SVD, as far as I know does dimensional reduction by…
spiralarchitect
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Convert a bivariate draw in a univariate draw in Matlab

I have in mind the following experiment to run in Matlab and I am asking for an help to implement step (3). Any suggestion would be very appreciated. (1) Consider the random variables X and Y both uniformly distributed on [0,1] (2) Draw N…
TEX
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Isomap "nonlinear dimensionality reduction" numbre of points

I have a question please , it's about 'Isomap' nonlinear dimensionality reduction, in normal cases when I introduce a matrix distance of 100 * 100 and I apply Isomap [http://isomap.stanford.edu/][1] I get the coordinates of 100 points ,in other…
sam Black
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Dimension Reduction (TSNE/PCA) on Sparse Matrix

I want to perform Dimension Reduction(DR) technique to visualize my data and how related they are to each other. I am planning to use Barnes-hut tsne but I am not able to get how to provide input to TSNE because the sample application has data in…
work_in_progress
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Matlab : Dimension reduction

LEt x_t = F(x_{t-1}) be a discret in time one dynamical system in chaotic regime. Starting from an initial condition x_0, we can generate a time series = x_t where t =1,2,...,T indicates the time index. s_t = 1 if x_t > c otherwise s_t = 0…
SKM
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how to use LSA for dimension reduction in text analytics with R

I am a beginner at data science, and I am working on a text analytics/sentiment analysis project with tweets. what i have been trying to do is to perform some dimension reduction on my tweets training set, and feed the training set into a NaiveBayes…
alwaysaskingquestions
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ProClus cluster analysis in R

For my thesis assignment I need to perform a cluster analysis on a high dimensional data set containing purchase data from a retail store (+1000 dimensions). Because traditional clustering algorithms are not well suited for high dimensions (and…
JaperTIA
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Deciding about dimensionality reduction, classification and clustering?

Could you please help me to understand it because I'm not sure if I got it correctly. Let's say I have a dataset, of persons, with 100 features, various characteristics like height, weight, age, etc. I want to classify if are normal or abnormal. By…
jimakos17
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Using PCA to project onto a lower dimensional space in Octave

I have the following matrix of size 300 x 2, which contains min-max normalised data: # Pre-Process data scaled_acc = preprocess(mtx_accuracy); # PCA on mtx_accuracy [pcvars pcvecs] = princomp(scaled_acc); preprocess is a scaling function: function…
Ælex
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dimensionality reduction algorithms

I have an data sheet with almost 2000 input parameters and 4 output parameters. I am to optimize the input parameters to define the output I am not sure whether the input parameters are linearly related so I am looking for a non linear…
shr m
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Feature Hashing / Avalanche Effect

I’ve been reading a bit about feature hashing for dimensionality reduction. I understand that it’s important to use a hash function that has a uniform output distribution (the chance of an input being mapped to a specific value is that same as every…
Robo Panic
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How to use Principle Component Analysis (PCA) for dimensionality reduction in matlab

I have 50 Matrices of data with 80*80 dimensions. I need to classify them, but before that I have to reduce the dimensionality of data. As I searched the web, the best tool is PCA. I know that before classification I have to transform each Matrix…
Araz
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supervised dimensionality redunction/topic model using sklearn or gensim

I've got BOW vectors and I'm wondering if there's a supervised dimensionality reduction algorithm in sklearn or gensim capable of taking high-dimensional, supervised data and projecting it into a lower dimensional space which preserves the variance…
Hao Fu
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Tag Clustering in Lastfm database

I have a last.fm dataset composed of songs and their tags given by the users. I want to apply a clusterization on the dataset in order to find clusters of songs based on tags. The dataset has 200k songs and 119k different tags. I was previously…
Thiago
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