I want to find the opinion of a sentence either positive or negative. For example talk about only one sentence.
The play was awesome
If change it to vector form
[0,0,0,0]
After searching through the Bag of words
bad
naughty
awesome
The vector form becomes
[0,0,0,1]
Same for other sentences. Now I want to pass it to the machine learning algorithm for training it. How can I train the network using these multiple vectors? (for finding the opinion of unseen sentences) Obviously not! Because the input is fix in neural network. Is there any way? The above procedure is just my thinking. Kindly correct me if I am wrong. Thanks in advance.
Since your intuitive input format is "Sentence". Which is, indeed, a string of tokens with arbitrary length. Abstracting sentences as token series is not a good choice for many existing algorithms only works on determined format of inputs.
Hence, I suggest try using tokenizer on your entire training set. This will give you vectors of length of the dictionary, which is fixed for given training set.
Because when the length of sentences vary drastically, then size of the dictionary always keeps stable.
Then you can apply Neural Networks(or other algorithms) to the tokenized vectors.
However, vectors generated by tokenizer is extremely sparse because you only work on sentences rather than articles.
You can try LDA (supervised, not PCA), to reduce the dimension as well as amplify the difference.
That will keep the essential information of your training data as well as express your data at fixed size, while this "size" is not too large.
By the way, you may not have to label each word by its attitude since the opinion of a sentence also depends on other kind of words.
Simple arithmetics on number of opinion-expressing words many leave your model highly biased. Better label the sentences and leave the rest job to classifiers.
For the confusions
PCA and LDA are Dimensional Reduction techniques.
difference
Let's assume each tuple of sample is denoted as x (1-by-p vector).
p is too large, we don't like that.
Let's find a matrix A(p-by-k) in which k is pretty small.
So we get reduced_x = x*A, and most importantly, reduced_x must
be able to represent x's characters.
Given labeled data, LDA can provide proper A that can maximize
distance between reduced_x of different classes, and also minimize
the distance within identical classes.
In simple words: compress data, keep information.
When you've got
reduced_x, you can define training data: (reduced_x|y) where y is
0 or 1.
In a lot of articles in my field, this sentence has been repeated: " The 2 matrices has been normalized to have the same average sum-of-squares (computed across all subjects and all voxels for each modality)". Suppose that we have two matrices that the rows define different subjects and the columns are features (voxels). In these articles, no much explanation can be found for normalization method. Does anybody knows how I should normalize data to have "same average sum-of-squares"? I don't understand it at all. Thanks
For a start normalization in this context is also known as features scaling, which pretty much sums it up. You scale your features, your data to get rid of variances and range of values which would disturb your algorithm and your results in the end.
https://en.wikipedia.org/wiki/Feature_scaling
In data processing, normalization is quite useful (depending on the application). E.g. in distance based machine learning algorithms you should normalize your features in order to get a proportional contribution to the outcome of your algorithm, independent of the range of value the features comprise.
To do so, you can use different statistical measurements, like the
Sum of squares:
SUM_i(Xi-Xbar)²
Other than that you could use the variance or the standard deviation of your data.
https://www.westgard.com/lesson35.htm#4
Those statistical terms can then be used to normalize your data, to improve e.g. the clustering quality of your algorithm. Which term to use and which method highly depends on the algorithms and data you're using and what you're aiming at.
Here is a paper which compares some of the approaches you could choose from for clustering:
http://maxwellsci.com/print/rjaset/v6-3299-3303.pdf
I hope this can help you a little.
My dataset is composed of millions of row and a couple (10's) of features.
One feature is a label composed of 1000 differents values (imagine each row is a user and this feature is the user's firstname :
Firstname,Feature1,Feature2,....
Quentin,1,2
Marc,0,2
Gaby,1,0
Quentin,1,0
What would be the best representation for this feature (to perform clustering) :
I could convert the data as integer using a LabelEncoder, but it doesn't make sense here since there is no logical "order" between two differents label
Firstname,F1,F2,....
0,1,2
1,0,2
2,1,0
0,1,0
I could split the feature in 1000 features (one for each label) with 1 when the label match and 0 otherwise. However this would result in a very big matrix (too big if I can't use sparse matrix in my classifier)
Quentin,Marc,Gaby,F1,F2,....
1,0,0,1,2
0,1,0,0,2
0,0,1,1,0
1,0,0,1,0
I could represent the LabelEncoder value as a binary in N columns, this would reduce the dimension of the final matrix compared to the previous idea, but i'm not sure of the result :
LabelEncoder(Quentin) = 0 = 0,0
LabelEncoder(Marc) = 1 = 0,1
LabelEncoder(Gaby) = 2 = 1,0
A,B,F1,F2,....
0,0,1,2
0,1,0,2
1,0,1,0
0,0,1,0
... Any other idea ?
What do you think about solution 3 ?
Edit for some extra explanations
I should have mentioned in my first post, but In the real dataset, the feature is the more like the final leaf of a classification tree (Aa1, Aa2 etc. in the example - it's not a binary tree).
A B C
Aa Ab Ba Bb Ca Cb
Aa1 Aa2 Ab1 Ab2 Ab3 Ba1 Ba2 Bb1 Bb2 Ca1 Ca2 Cb1 Cb2
So there is a similarity between 2 terms under the same level (Aa1 Aa2 and Aa3are quite similar, and Aa1 is as much different from Ba1 than Cb2).
The final goal is to find similar entities from a smaller dataset : We train a OneClassSVM on the smaller dataset and then get a distance of each term of the entiere dataset
This problem is largely one of one-hot encoding. How do we represent multiple categorical values in a way that we can use clustering algorithms and not screw up the distance calculation that your algorithm needs to do (you could be using some sort of probabilistic finite mixture model, but I digress)? Like user3914041's answer, there really is no definite answer, but I'll go through each solution you presented and give my impression:
Solution 1
If you're converting the categorical column to an numerical one like you mentioned, then you face that pretty big issue you mentioned: you basically lose meaning of that column. What does it really even mean if Quentin in 0, Marc 1, and Gaby 2? At that point, why even include that column in the clustering? Like user3914041's answer, this is the easiest way to change your categorical values into numerical ones, but they just aren't useful, and could perhaps be detrimental to the results of the clustering.
Solution 2
In my opinion, depending upon how you implement all of this and your goals with the clustering, this would be your best bet. Since I'm assuming you plan to use sklearn and something like k-Means, you should be able to use sparse matrices fine. However, like imaluengo suggests, you should consider using a different distance metric. What you can consider doing is scaling all of your numeric features to the same range as the categorical features, and then use something like cosine distance. Or a mix of distance metrics, like I mention below. But all in all this will likely be the most useful representation of your categorical data for your clustering algorithm.
Solution 3
I agree with user3914041 in that this is not useful, and introduces some of the same problems as mentioned with #1 -- you lose meaning when two (probably) totally different names share a column value.
Solution 4
An additional solution is to follow the advice of the answer here. You can consider rolling your own version of a k-means-like algorithm that takes a mix of distance metrics (hamming distance for the one-hot encoded categorical data, and euclidean for the rest). There seems to be some work in developing k-means like algorithms for mixed categorical and numerical data, like here.
I guess it's also important to consider whether or not you need to cluster on this categorical data. What are you hoping to see?
Solution 3:
I'd say it has the same kind of drawback as using a 1..N encoding (solution 1), in a less obvious fashion. You'll have names that both give a 1 in some column, for no other reason than the order of the encoding...
So I'd recommend against this.
Solution 1:
The 1..N solution is the "easy way" to solve the format issue, as you noted it's probably not the best.
Solution 2:
This looks like it's the best way to do it but it is a bit cumbersome and from my experience the classifier does not always performs very well with a high number of categories.
Solution 4+:
I think the encoding depends on what you want: if you think that names that are similar (like John and Johnny) should be close, you could use characters-grams to represent them. I doubt this is the case in your application though.
Another approach is to encode the name with its frequency in the (training) dataset. In this way what you're saying is: "Mainstream people should be close, whether they're Sophia or Jackson does not matter".
Hope the suggestions help, there's no definite answer to this so I'm looking forward to see what other people do.
this question troubles me for two days. Now i am comparing the similarity of two time series data. The approach i know so far is to calculate the distance between them. Here, i choose the Dynamic Time Warping(DTW) to compute their distance. As a result, there is a warping path together with their DTW distance. Now my question is, how can i judge whether these two are similar based on this distance? Is there any threshold defined for this problem?
My intuition tells me that, if they are identical, then the distance between them would be 0.
Can anyone help me deal with this question?
Why not just use some simple statistical methods like finding the correlation between the two sets of data? You could do this in Excel quite easily - see this tutorial http://www.excel-easy.com/examples/correlation.html
Using distance measure on Time Series is always risky and yes, you need to define some threshold. The value will depend on your data. (It is all hit and trial approach).
Further,You can also refer to the paper "A review on time series data mining".
link:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.454.773&rep=rep1&type=pdf
In the paper, you can find various approach to find the similarity in Time Series
I need to write a program that performs arithmetic (+-*/) on multiples time series of different date range (mostly from 2007-2009) and frequency (weekly, monthly, yearly...).
I came up with:
find the series with the highest freq. then fill in the other series with zeros so they have the same number of elements. then perform the operation.
How can I present the data in the most meaningful way?
Trying to think of all the possibilities
If zero can be a meaningful value for this time series (e.g. temperature in Celsius degrees), it might not be a good idea to fill all gaps with zeros (i.e. you will not be able to distinguish between the real and stub values afterwards). You might want to interpolate your time series. Basic data structure for this can be array/double linked list.
You can take several approaches:
use the finest-grained time series data (for instance, seconds) and interpolate/fill data when needed
use the coarsest-grained (for instance, years) and summarize data when needed
any middle step between the two extremes
You should always know your data, because:
in case of interpolating you have to choose the best algorithm (linear or quadratic interpolation, splines, exponential...)
in case of summarizing you have to choose an appropriate aggregation function (sum, maximum, mean...)
Once you have the same time scales for all the time series you can perform your arithmetical magick, but be aware that interpolation generates extra information, and summarization removes available information.
I've studied this problem fairly extensively. The danger of interpolation methods is that you bias various measures - especially volatility - and introduce spurious correlation. I found that Fourier interpolation mitigated this to some extent but the better approach is to go the other way: aggregate your more frequent observations to match the periodicity of your less frequent series, then compare these.