Questions tagged [sparsity]
9 questions
2
votes
0 answers
Loss function for Autoencoder of sparse 3D Image
I have 3D structure data of molecules. I represented the atoms as points in a 100*100*100 grid and applied a gaussian blur to counter the sparseness. (nearly all of the grid cells contain zeros) I am trying to build an autoencoder to get a…
PascalIv
- 433
- 2
- 8
1
vote
1 answer
Autoencoder train and test accuracy shooting to 99% on few epochs
I am trying to train an autoencoder for dimensionality reduction and hopefully for anomaly detection. My data specifications are as follows.
Unlabeled
1 million data points
9 features
I am trying to reduce it to 2 compressed features so I can have…
1
vote
2 answers
What is the meaning of the sparsity parameter
Sparse methods such as LASSO contain a parameter $\lambda$ which is associated with the minimization of the $l_1$ norm. Higher the value of $\lambda$ ($>0$) means that more coefficients will be shrunk to zero. What is unclear to me is that how does…
Sm1
- 511
- 3
- 17
1
vote
0 answers
If $\ell_0$ regularization can be done via the proximal operator, why are people still using LASSO?
I have just learned that a general framework in constrained optimization is called "proximal gradient optimization". It is interesting that the $\ell_0$ "norm" is also associated with a proximal operator. Hence, one can apply iterative hard…
ArtificiallyIntelligence
- 282
- 3
- 10
1
vote
1 answer
Why is the L2 penalty squared but the L1 penalty isn't in elastic-net regression?
There was some data set I worked with which I wanted to solve non negative least squares (NNLS) on and I wanted a sparse model. After a bit of experiementing I found that what worked the best for me was using the following loss function:
$$\min_{x…
Tomer Wolberg
- 111
- 3
0
votes
1 answer
What is the difference between sparse and dense corpra?
I didn't got the meaning or the difference between sparse and dense corpra here in this sentence "the reason is that Skip-gram works better over sparse corpora like Twitter and NIPS, while CBOW works better over dense corpora "
user
- 1
0
votes
1 answer
Clustering of sparse matrix with many co-variates
I have a 2M x 2000 sparse matrix where rows represent an item and columns represent dimensions. I want to understand whether there are meaningful clusters in the data and I started to explore the dimensions to transform and normalise the data.
Of…
Strabonio
- 173
- 5
0
votes
0 answers
Is there a specific gain from using a dataset of sparse images in CNN training instead of regular images?
By sparse images, I mean images where each R, G, B value is either 0 or 1. Does this contribute in faster training or any other process of NN training? My guess would be that having multiple nodes multiplied by 0s (and consequently being dropped)…
Amadeo Amadei
- 1
- 1
0
votes
0 answers
What is the disadvantage of sparse NLP models?
I was reading this paper -- in preparation for a job interview -- and I was trying to determine what the advantages and disadvantages of the approach were. So I feel that the advantages were stated by the author. But the disadvantages-- I could only…
yishairasowsky
- 157
- 1
- 6