Suppose there is an image containing multiple objects of different types. The objective of the problem is to recognize objects using primary features of objects (colour, texture, shape). Explain your own idea what concepts will you apply, and how will you apply them, to differentiate/classify the objects in the image by extracting primary features (or combination of features) of objects. Also, justify how your idea can produce the best accuracy.
Since this is a theoretical question, it can have many answers. The simplest approach is to use k-means or weighted k-means, using the features you have. If you have quite unique features then k-means would be able to classify decently accurately. You might still have to juggle around finding how you would input some of the more esoteric features to k-means though. Other more involved methods would use your own trained model using CNN for classification using the features you provide.
Since this is a theoretical question this is all the answer I can provide you with.
I'm attempting to use RBM neural network in sklearn, but I can't find a predict function, I see how you can train it (I think) but I can't seem to figure out how to actually predict a value.
http://scikit-learn.org/stable/auto_examples/neural_networks/plot_rbm_logistic_classification.html#example-neural-networks-plot-rbm-logistic-classification-py
I'm working on a class assignment. this is the assignment:
You will then use randomized hill climbing algorithm to find good weights for a neural network.
Is it possible to do this with SKLearn? Is there a better recommended tool to be able to select different weights for NN? (The goal is to experiment with around 3 different search optimization techniques, and learn about them, not necessarily write them, nor write a NN in this case).
RBM's do not do prediction tasks. They are generative models. You can use the transform method to get a hidden state transformation of the input, or your gan use the gibbs method to sample from the network.
You will then use randomized hill climbing algorithm to find good weights for a neural network.
No, this is not available in scikit-learn.
It sounds like your assignment might be meant for you to implement a simpler problem from scratch rather than use another library, as hill climbing isn't normally used for training a neural network. And they probably don't want you to do hill climbing for an RBM neural network.
You should probably consult your professor for more direction on what you really should be doing.
I'm performing an experiment in which I need to compare classification performance of several classification algorithms for spam filtering, viz. Naive Bayes, SVM, J48, k-NN, RandomForests, etc. I'm using the WEKA data mining tool. While going through the literature I came to know about various dimension reduction methods which can be broadly classified into two types-
Feature Reduction: Principal Component Analysis, Latent Semantic Analysis, etc.
Feature Selection: Chi-Square, InfoGain, GainRatio, etc.
I have also read this tutorial of WEKA by Jose Maria in his blog: http://jmgomezhidalgo.blogspot.com.es/2013/02/text-mining-in-weka-revisited-selecting.html
In this blog he writes, "A typical text classification problem in which dimensionality reduction can be a big mistake is spam filtering". So, now I'm confused whether dimensionality reduction is of any use in case of spam filtering or not?
Further, I have also read in the literature about Document Frequency and TF-IDF as being one of feature reduction techniques. But I'm not sure how does it work and come into play during classification.
I know how to use weka, chain filters and classifiers, etc. The problem I'm facing is since I don't have enough idea about feature selection/reduction (including TF-IDF) I am unable to decide how and what feature selection techniques and classification algorithms I should combine to make my study meaningful. I also have no idea about optimal threshold value that I should use with chi-square, info gain, etc.
In StringToWordVector class, I have an option of IDFTransform, so does it makes sence to set it to TRUE and also use a feature selection technique, say InfoGain?
Please guide me and if possible please provide links to resources where I can learn about dimension reduction in detail and can plan my experiment meaningfully!
Well, Naive Bayes seems to work best for spam filtering, and it doesn't play nicely with dimensionality reduction.
Many dimensionality reduction methods try to identify the features of the highest variance. This of course won't help a lot with spam detection, you want discriminative features.
Plus, there is not only one type of spam, but many. Which is likely why naive Bayes works better than many other methods that assume there is only one type of spam.
I need 3 underlying papers / most top tree in regard to MEAN SHIFT, OPTICAL FLOW, KALMAN FILTER.
I've searched in ieee xplore, it showed many related papers.
Any idea?
Thanks in advance.
Do you know about CiteSeerX?
For Mean Shift I get Mean shift: A robust approach toward feature space analysis, which is a very good paper on that topic.
For the other topics I cannot help you, but you generally find good papers by reading papers and looking at the references.
These are old unsolved yet classic Computer Vision problems:
Mean Shift
Mean shift: A robust approach toward feature space analysis [same as bjoernz] but in practice, I would prefer a completely different unsupervised segmentation work from Felzenszwalb et al. Efficient graph-based segmentation (faster + better)
Optical Flow
Sparse reliable points: Good Features to track is a nice summary of what is called the KLT literature (for Kanade-Lucas-Tomasi ... poor Jianbo Shi). In a nutshell, some points (corners) in your images are easier to track than others in uniform regions for example.
Dense for each pixel: Horn-Schunk historical paper but check out recent Thomas Brox and Jitendra Malik works and also the one Ce Liu also publish.
Kalman filter: Historical Paper but I do not think it is still cited a lot because everybody seems to refer to their favorite textbooks instead.
For efficient implementations of almost all these nice articles: OpenCV at the rescue!
Caveat: Machine Learning people who are very trendy in Computer Vision these days are sometimes confused by the word features. Indeed, one can distinguish:
Detectors: that selects sparse points in the image ( corners like e.g. Hessian, Harris ...)
Descriptors: that describe these points and also the image through concatenation
Features space: a fancy way to describe their kernel-SVM stuff for recognition
For example, SIFT is both a detector and a descriptor technique although it is called a feature.
Closed. This question is off-topic. It is not currently accepting answers.
Want to improve this question? Update the question so it's on-topic for Stack Overflow.
Closed 10 years ago.
Improve this question
I have a few sets of questions regarding outlier detection:
Can we find outliers using k-means and is this a good approach?
Is there any clustering algorithm which does not accept any input from the user?
Can we use support vector machine or any other supervised learning algorithm for outlier detection?
What are the pros and cons of each approach?
I will limit myself to what I think is essential to give some clues about all of your questions, because this is the topic of a lot of textbooks and they might probably be better addressed in separate questions.
I wouldn't use k-means for spotting outliers in a multivariate dataset, for the simple reason that the k-means algorithm is not built for that purpose: You will always end up with a solution that minimizes the total within-cluster sum of squares (and hence maximizes the between-cluster SS because the total variance is fixed), and the outlier(s) will not necessarily define their own cluster. Consider the following example in R:
set.seed(123)
sim.xy <- function(n, mean, sd) cbind(rnorm(n, mean[1], sd[1]),
rnorm(n, mean[2],sd[2]))
# generate three clouds of points, well separated in the 2D plane
xy <- rbind(sim.xy(100, c(0,0), c(.2,.2)),
sim.xy(100, c(2.5,0), c(.4,.2)),
sim.xy(100, c(1.25,.5), c(.3,.2)))
xy[1,] <- c(0,2) # convert 1st obs. to an outlying value
km3 <- kmeans(xy, 3) # ask for three clusters
km4 <- kmeans(xy, 4) # ask for four clusters
As can be seen in the next figure, the outlying value is never recovered as such: It will always belong to one of the other clusters.
One possibility, however, would be to use a two-stage approach where one's removing extremal points (here defined as vector far away from their cluster centroids) in an iterative manner, as described in the following paper: Improving K-Means by Outlier Removal (Hautamäki, et al.).
This bears some resemblance with what is done in genetic studies to detect and remove individuals which exhibit genotyping error, or individuals that are siblings/twins (or when we want to identify population substructure), while we only want to keep unrelated individuals; in this case, we use multidimensional scaling (which is equivalent to PCA, up to a constant for the first two axes) and remove observations above or below 6 SD on any one of say the top 10 or 20 axes (see for example, Population Structure and Eigenanalysis, Patterson et al., PLoS Genetics 2006 2(12)).
A common alternative is to use ordered robust mahalanobis distances that can be plotted (in a QQ plot) against the expected quantiles of a Chi-squared distribution, as discussed in the following paper:
R.G. Garrett (1989). The chi-square plot: a tools for multivariate outlier recognition. Journal of Geochemical Exploration 32(1/3): 319-341.
(It is available in the mvoutlier R package.)
It depends on what you call user input. I interpret your question as whether some algorithm can process automatically a distance matrix or raw data and stop on an optimal number of clusters. If this is the case, and for any distance-based partitioning algorithm, then you can use any of the available validity indices for cluster analysis; a good overview is given in
Handl, J., Knowles, J., and Kell, D.B.
(2005). Computational cluster validation in post-genomic data analysis.
Bioinformatics 21(15): 3201-3212.
that I discussed on Cross Validated. You can for instance run several instances of the algorithm on different random samples (using bootstrap) of the data, for a range of cluster numbers (say, k=1 to 20) and select k according to the optimized criteria taht was considered (average silhouette width, cophenetic correlation, etc.); it can be fully automated, no need for user input.
There exist other forms of clustering, based on density (clusters are seen as regions where objects are unusually common) or distribution (clusters are sets of objects that follow a given probability distribution). Model-based clustering, as it is implemented in Mclust, for example, allows to identify clusters in a multivariate dataset by spanning a range of shape for the variance-covariance matrix for a varying number of clusters and to choose the best model according to the BIC criterion.
This is a hot topic in classification, and some studies focused on SVM to detect outliers especially when they are misclassified. A simple Google query will return a lot of hits, e.g. Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction by Thongkam et al. (Lecture Notes in Computer Science 2008 4977/2008 99-109; this article includes comparison to ensemble methods). The very basic idea is to use a one-class SVM to capture the main structure of the data by fitting a multivariate (e.g., gaussian) distribution to it; objects that on or just outside the boundary might be regarded as potential outliers. (In a certain sense, density-based clustering would perform equally well as defining what an outlier really is is more straightforward given an expected distribution.)
Other approaches for unsupervised, semi-supervised, or supervised learning are readily found on Google, e.g.
Hodge, V.J. and Austin, J. A Survey of Outlier Detection Methodologies.
Vinueza, A. and Grudic, G.Z. Unsupervised Outlier Detection and Semi-Supervised Learning.
Escalante, H.J. A Comparison of Outlier Detection Algorithms for Machine Learning.
A related topic is anomaly detection, about which you will find a lot of papers.
That really deserves a new (and probably more focused) question :-)
1) Can we find outliers using k-means, is it a good approach?
Cluster-based approaches are optimal to find clusters, and can be used to detect outliers as
by-products. In the clustering processes, outliers can affect the locations of the cluster centers, even aggregating as a micro-cluster. These characteristics make the cluster-based approaches infeasible to complicated databases.
2) Is there any clustering algorithm which does not accept any input from the user?
Maybe you can achieve some valuable knowledge on this topic:
Dirichlet Process Clustering
Dirichlet-based clustering algorithm can adaptively determine the number of clusters according to the distribution of observation data.
3) Can we use support vector machine or any other supervised learning algorithm for outlier detection?
Any Supervised learning algorithm needs enough labeled training data to construct classifiers. However, a balanced training dataset is not always available for real world problem, such as intrusion detection, medical diagnostics. According to the definition of Hawkins Outlier("Identification of Outliers". Chapman and Hall, London, 1980), the number of normal data is much larger than that of outliers. Most supervised learning algorithms can't achieve an efficient classifier on the above unbalanced dataset.
4) What is the pros and cons of each approach?
Over the past several decades, the research on outlier detection varies from the global computation to the local analysis, and the descriptions of outliers vary from the binary interpretations to probabilistic representations. According to hypotheses of outlier detection models, outlier detection algorithms can be divided into four kinds: Statistic-based algorithms, Cluster-based algorithms, Nearest Neighborhood based algorithms, and Classifier-based algorithms. There are several valuable surveys on outlier detection:
Hodge, V. and Austin, J. "A survey of outlier detection methodologies", Journal of Artificial Intelligence Review, 2004.
Chandola, V. and Banerjee, A. and Kumar, V. "Outlier detection: A survey", ACM Computing Surveys, 2007.
k-means is rather sensitive to noise in the data set. It works best when you remove the outliers beforehand.
No. Any cluster analysis algorithm that claims to be parameter-free usually is heavily restricted, and often has hidden parameters - a common parameter is the distance function, for example. Any flexible cluster analysis algorithm will at least accept a custom distance function.
one-class classifiers are a popular machine-learning approach to outlier detection. However, supervised approaches aren't always appropriate for detecting _previously_unseen_ objects. Plus, they can overfit when the data already contains outliers.
Every approach has its pros and cons, that is why they exist. In a real setting, you will have to try most of them to see what works for your data and setting. It's why outlier detection is called knowledge discovery - you have to explore if you want to discover something new ...
You may want to have a look at the ELKI data mining framework. It is supposedly the largest collection of outlier detection data mining algorithms. It's open source software, implemented in Java, and includes some 20+ outlier detection algorithms. See the list of available algorithms.
Note that most of these algorithms are not based on clustering. Many clustering algorithms (in particular k-means) will try to cluster instances "no matter what". Only few clustering algorithms (e.g. DBSCAN) actually consider the case that maybe not all instance belong into clusters! So for some algorithms, outliers will actually prevent a good clustering!