Most recent object detection methods rely on a convolutional neural network. They create a feature map by running input data through a feature extraction step. They then add more convolutional layers to output a set of values like so (this set is from YOLO, but other architectures like SSD differ slightly):
pobj: probability of being an object
c1, c2 ... cn: indicating which class the object belongs to
x, y, w, h: bounding box of the object
However, one particular box cannot be multiple objects. As in, wouldn't having a high value for, say, c1 mean that the values for all the others c2 ... cn would be low? So why use different values for c1, c2 ... cn? Couldn't they all be represented by a single value, say 0-1, where each object has a certain range within the 0-1, say 0-0.2 is c1, 0.2-0.4 is c2 and so on...
This would reduce the dimension of the output from NxNx(5+C) (5 for the probability and bounding box, +C one for each class) to NxNx(5+1) (5 same as before and 1 for the class)
Thank you
Short answer, NO! That is almost certainly not an acceptable solution. It sounds like your core question is: Why is a a single value in the range [0,1] not a sufficient, compact output for object classification? As a clarification, I'd say this doesn't really have to do with single-shot detectors; the outputs from 2-stage detectors and most all classification networks follows this same 1D embedding structure. As a secondary clarification, I'd say that many 1-stage networks also don't output pobj in their original implementations (YOLO is the main one that does but Retinanet and I believe SSD does not).
An object's class is a categorical attribute. Assumed within a standard classification problem is that the set of possible classes is flat (i.e. no class is a subclass of any other), mutually exclusive (each example falls into only a single class), and unrelated (not quite the right term here but essentially no class is any more or less related to any other class).
This assumed attribute structure is well represented by an orthonormal encoding vector of the same length as the set of possible attributes. A vector [1,0,0,0] is no more similar to [0,1,0,0] than it is to [0,0,0,1] in this space.
(As an aside, a separate branch of ML problems called multilabel classification removes the mutual exclusivity constrain (so [0,1,1,0] and [0,1,1,1] would both be valid label predictions. In this space class or label combinations COULD be construed as more or less related since they share constituent labels or "basis vectors" in the orthonormal categorical attribute space. But enough digression..)
A single, continuous variable output for class destroys the assumption that all classes are unrelated. In fact, it assumes that the relation between any two classes is exact and quantifiable! What an assumption! Consider attempting to arrange the classes of, let's say, the ImageNet classification task, along a single dimension. Bus and car should be close, no? Let's say 0.1 and 0.2, respectively in our 1D embedding range of [0,1]. Zebra must be far away from them, maybe 0.8. But should be close to zebra fish (0.82)? Is a striped shirt closer to a zebra or a bus? Is the moon more similar to a bicycle or a trumpet? And is a zebra really 5 times more similar to a zebra fish than a bus is to a car? The exercise is immediately, patently absurd. A 1D embedding space for object class is not sufficiently rich to capture the differences between object classes.
Why can't we just place object classes randomly in the continuous range [0,1]? In a theoretical sense nothing is stopping you, but the gradient of the network would become horrendously, unmanageably non-convex and conventional approaches to training the network would fail. Not to mention the network architecture would have to encode extremely non-linear activation functions to predict the extremely hard boundaries between neighboring classes in the 1D space, resulting in a very brittle and non-generalizable model.
From here, the nuanced reader might suggest that in fact, some classes ARE related to one another (i.e. the unrelated assumption of the standard classification problem is not really correct). Bus and car are certainly more related than bus and trumpet, no? Without devolving into a critique on the limited usefulness of strict ontological categorization of the world, I'll simply suggest that in many cases there is an information embedding that strikes a middle ground. A vast field of work has been devoted to finding embedding spaces that are compact (relative to the exhaustive enumeration of "everything is its own class of 1") but still meaningful. This is the work of principal component analysis and object appearance embedding in deep learning.
Depending on the particular problem, you may be able to take advantage of a more nuanced embedding space better suited towards the final task you hope to accomplish. But in general, canonical deep learning tasks such as classification / detection ignore this nuance in the hopes of designing solutions that are "pretty good" generalized over a large range of problem spaces.
For object classification head, usually cross-entropy loss function is used which operates on the probability distribution to compute the difference between ground-truth(a one hot encoded vector) and prediction class scores.
On the otherhand, you are proposing a different way of encoding the ground-truth class labels which can be further used with certain custom loss function say L1/l2 loss function, which looks theoretically correct but it might not be as good as cross-entropy function in terms of model convergence/optimization.
I am trying to generate a model that uses several physico-chemical properties of a molecule (incl. number of atoms, number of rings, volume, etc.) to predict a numeric value Y. I would like to use PLS Regression, and I understand that standardization is very important here. I am programming in Python, using scikit-learn. The type and range for the features varies. Some are int64 while other are float. Some features generally have small (positive or negative) values, while other have very large value. I have tried using various scalers (e.g. standard scaler, normalize, minmax scaler, etc.). Yet, the R2/Q2 are still low. I have a few questions:
Is it possible that by scaling, some of the very important features lose their significance, and thus contribute less to explaining the variance of the response variable?
If yes, if I identify some important features (by expert knowledge), is it OK to scale other features but those? Or scale the important features only?
Some of the features, although not always correlated, have values that are in a similar range (e.g. 100-400), compared to others (e.g. -1 to 10). Is it possible to scale only a specific group of features that are within the same range?
The whole idea of scaling is to make models more robust to analysis on features space. For example, if you have 2 features as 5 Kg and 5000 gm, we know both are same, but for some algorithm, which are sensitive to metric space such as KNN, PCA etc, they will be more weighted towards second features, so scaling must be done for these algos.
Now coming to your question,
Scaling doesn't effect the significance of features. As i explained above, it helps in better analysis of data.
No, you should not do, reason explained above.
If you want to include domain knowledge in your model, you can use it as prior information. In short, for linear model, this is same as regularization. It has very good features. if you think, you have many useless-features, you can use L1 regularization, which creates sparse effect on features space, which is nothing but assign 0 weight to useless features. Here is the link for more-info.
One more point, some method such as tree based model doesn't need scaling, In last, it mostly depend on the model, you choose.
Lose significance? Yes. Contribute less? No.
No, it's not OK. It's either all or nothing.
No. The idea of scaling is not to decrease / increase significance / effect of a variable. It's to transform all variables to a common scale that can be interpreted.
I have multiple sets with a variable number of sequences. Each sequence is made of 64 numbers that are either 0 or 1 like so:
Set A
sequence 1: 0,0,0,0,0,0,1,1,0,0,0,0,1,1,1,1,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0
sequence 2:
0,0,0,0,1,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
sequence 3:
0,0,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0
...
Set B
sequence1:
0,0,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1
sequence2:
0,0,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,0
...
I would like to find a mathematical function that describes all possible sequences in the set, maybe even predict more and that does not contain the sequences in the other sets.
I need this because I am trying to recognize different gestures in a mobile app based on the cells in a grid that have been touched (1 touch/ 0 no touch). The sets represent each gesture and the sequences a limited sample of variations in each gesture.
Ideally the function describing the sequences in a set would allow me to test user touches against it to determine which set/gesture is part of.
I searched for a solution, either using Excel or Mathematica, but being very ignorant about both and mathematics in general I am looking for the direction of an expert.
Suggestions for basic documentation on the subject is also welcome.
It looks as if you are trying to treat what is essentially 2D data in 1D. For example, let s1 represent the first sequence in set A in your question. Then the command
ArrayPlot[Partition[s1, 8]]
produces this picture:
The other sequences in the same set produce similar plots. One of the sequences from the second set produces, in response to the same operations, the picture:
I don't know what sort of mathematical function you would like to define to describe these pictures, but I'm not sure that you need to if your objective is to recognise user gestures.
You could do something much simpler, such as calculate the 'average' picture for each of your gestures. One way to do this would be to calculate the average value for each of the 64 pixels in each of the pictures. Perhaps there are 6 sequences in your set A describing gesture A. Sum the sequences element-by-element. You will now have a sequence with values ranging from 0 to 6. Divide each element by 6. Now each element represents a sort of probability that a new gesture, one you are trying to recognise, will touch that pixel.
Repeat this for all the sets of sequences representing your set of gestures.
To recognise a user gesture, simply compute the difference between the sequence representing the gesture and each of the sequences representing the 'average' gestures. The smallest (absolute) difference will direct you to the gesture the user made.
I don't expect that this will be entirely foolproof, it may well result in some user gestures being ambiguous or not recognisable, and you may want to try something more sophisticated. But I think this approach is simple and probably adequate to get you started.
In Mathematica the following expression will enumerate all the possible combinations of {0,1} of length 64.
Tuples[{1, 0}, {64}]
But there are 2^62 or 18446744073709551616 of them, so I'm not sure what use that will be to you.
Maybe you just wanted the unique sequences contained in each set, in that case all you need is the Mathematica Union[] function applied to the set. If you have a the sets grouped together in a list in Mathematica, say mySets, then you can apply the Union operator to every set in the list my using the map operator.
Union/#mySets
If you want to do some type of prediction a little more information might be useful.
Thanks you for the clarifications.
Machine Learning
The task you want to solve falls under the disciplines known by a variety of names, but probably most commonly as Machine Learning or Pattern Recognition and if you know which examples represent the same gestures, your case would be known as supervised learning.
Question: In your case do you know which gesture each example represents ?
You have a series of examples for which you know a label ( the form of gesture it is ) from which you want to train a model and use that model to label an unseen example to one of a finite set of classes. In your case, one of a number of gestures. This is typically known as classification.
Learning Resources
There is a very extensive background of research on this topic, but a popular introduction to the subject is machine learning by Christopher Bishop.
Stanford have a series of machine learning video lectures Standford ML available on the web.
Accuracy
You might want to consider how you will determine the accuracy of your system at predicting the type of gesture for an unseen example. Typically you train the model using some of your examples and then test its performance using examples the model has not seen. The two of the most common methods used to do this are 10 fold Cross Validation or repeated 50/50 holdout. Having a measure of accuracy enables you to compare one method against another to see which is superior.
Have you thought about what level of accuracy you require in your task, is 70% accuracy enough, 85%, 99% or better?
Machine learning methods are typically quite sensitive to the specific type of data you have and the amount of examples you have to train the system with, the more examples, generally the better the performance.
You could try the method suggested above and compare it against a variety of well proven methods, amongst which would be Random Forests, support vector machines and Neural Networks. All of which and many more are available to download in a variety of free toolboxes.
Toolboxes
Mathematica is a wonderful system, is infinitely flexible and my favourite environment, but out of the box it doesn't have a great deal of support for machine learning.
I suspect you will make a great deal of progress more quickly by using a custom toolbox designed for machine learning. Two of the most popular free toolboxes are WEKA and R both support more than 50 different methods for solving your task along with methods for measuring the accuracy of the solutions.
With just a little data reformatting, you can convert your gestures to a simple file format called ARFF, load them into WEKA or R and experiment with dozens of different algorithms to see how each performs on your data. The explorer tool in WEKA is definitely the easiest to use, requiring little more than a few mouse clicks and typing some parameters to get started.
Once you have an idea of how well the established methods perform on your data you have a good starting point to compare a customised approach against should they fail to meet your criteria.
Handwritten Digit Recognition
Your problem is similar to a very well researched machine learning problem known as hand written digit recognition. The methods that work well on this public data set of handwritten digits are likely to work well on your gestures.