I am trying to list problems of pattern recognition in real life. let me know if you can share some if you are on the field. Thanks
Please see some reference links on Challenges in pattern recognition.
Two challenges of correct validation in pattern recognition
https://www.frontiersin.org/articles/10.3389/frobt.2014.00005/full
Some Pattern Recognition Challenges in Data-Intensive Astronomy
https://ieeexplore.ieee.org/document/1699025
Challenges of Pattern Recognition in Biomedical Data
https://psb.stanford.edu/psb-online/proceedings/psb18/intro-challenges.pdf
Some Pattern Recognition Challenges in Data-Intensive Astronomy
https://authors.library.caltech.edu/22105/
Pattern recognition system: the challenges and progress
https://gtms1311.wordpress.com/2013/03/04/pattern-recognition-system-the-challenges-and-progress/
Some Interesting Problems in Pattern Recognition and Image Processing
https://web.csulb.edu/~jchang9/files/patternRecTalkUSC.pdf
One interesting and challenge problem is about sequence learning.
Our daily life is about time. So time series is classic and meaningful today and in the future.
How to mine the time dependencies for the Pattern Analysis is useful for NLP, Speech, spatial-temporal data mining and so on. One promising method given massive data is RNN based, such as LSTM and GRU. In my opinion, next generation RNN is of great importance and extremely challenging.
See this paper:
Fusion Recurrent Neural Network , maybe useful.
Fusion RNN is a novel, simple and effective RNN.
Restricted Recurrent Neural Networks is also an alternative. You can also look at this paper if you like.
Lots of challenges, in particular, best data cleaning methods are desired for computer vision applications. Another main focus is on the efficient mathematical/statistical models for pattern analysis.
There are two challenges. One is training data - there is no guarantee that the data covers unseen data content. The second problem is feature selection. Again there is no guarantee that the features will be present in unseen data. In real life data varies considerably.
The most important challenge is how to choose the suitable techniques for your problem
The main challenge is that the mathematical data model and its predictions may not produce a proper mapping with the physical visual / audio perceptual experiences.
Data quality and consistency are crucial challenges. Too much data homogeneity leads to poor generalization. Too much heterogeneity leads to high complexity methods to be modeled.
I think two important challenges are correct validation and proper techniques to avoid overfitting. Some examples are given in the following article:
https://www.frontiersin.org/articles/10.3389/frobt.2014.00005/full
The problem with training data is that there is no guarantee that it covers unseen candidate data.
I'm suggesting this for non-stationary data set. you can introduce cross-correlation method to compare the pattern with the reference segment. to minimize the time of recognition, introduce a percentage of accuracy for the recognition loop...
I ll send some pics of pattern recognition done by me to identify sound wave recorded by three microphones in order to find the time of arrival
The following links may be helpful :
Article Pattern Recognition Techniques: A Review
https://www.frontiersin.org/articles/10.3389/frobt.2014.00005/full
Dear;
for more information please check the following documents:
https://www.frontiersin.org/articles/10.3389/frobt.2014.00005/full
Too many challenges, including, reliable fast computing algorithms, data tractability to reliable statistical models, mathematical/statistical based optimization methods/techniques/algorithms for data analysis. Efficient techniques for data pre-processing techniques, e.g., linearization, local discretization, wavelet transforms, Laplace transforms, linear transformation, PCA/SVD etc. Sufficient data generation for reliable algorithms....etc.
There are two major issues in visual pattern recognition. The first is the choice of features. There is no guarantee that the features will be strongly present in unseen data. Secondly the choice of training data will not guarantee the coverage of unseen data. These issues are not present in artificial problems like chess where all the possibilities are well defined.
check this paper, challenges section
Article Review of deep learning: concepts, CNN architectures, challe...
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