Table of Contents
What are the issues associated with high dimensional data?
Dimensionally cursed phenomena occur in domains such as numerical analysis, sampling, combinatorics, machine learning, data mining and databases. The common theme of these problems is that when the dimensionality increases, the volume of the space increases so fast that the available data become sparse.
What is a good research topic for statistics?
A study of regression analysis. A statistical analysis on how the income gap between partners affect their relationship. A case study on the effect of festivals on Indian gold prices. Testing the untested waters; statistical discrepancies and possible occurrences.
What are interesting topics in statistics?
Missing Data & Observational Data Modeling. Record Linkage & Machine Learning. Small Area Estimation. Sampling Estimation & Survey Inference. Time Series & Seasonal Adjustment.
What is a high dimensional data set?
High dimensional data refers to a dataset in which the number of features p is larger than the number of observations N, often written as p >> N. A dataset could have 10,000 features, but if it has 100,000 observations then it’s not high dimensional.
What is high-dimensional data example?
High dimension is when variable numbers p is higher than the sample sizes n i.e. p>n, cases. For example, tomographic imaging data, ECG data, and MEG data. One example of high dimensional data is microarray gene expression data.
Why Knn might fail for high-dimensional feature spaces?
k-nearest neighbors doesn’t work that way. It needs all points to be close along every axis in the data space. And each new axis added, by adding a new dimension, makes it harder and harder for two specific points to be close to each other in every axis.
What is research questions in statistics?
A research question is ‘a question that a research project sets out to answer’. Choosing a research question is an essential element of both quantitative and qualitative research. Investigation will require data collection and analysis, and the methodology for this will vary widely.
Is image high-dimensional data?
Regardless of whether this data is processed as an image, video, text, speech, or purely numeric, it almost always exists in some high-dimensional space.
What is high-dimensional flow cytometry?
High-dimensional flow cytometry and mass cytometry (or CyTOF, for “cytometry by time-of-flight mass spectrometry”) characterize cell types and states by measuring expression levels of pre-defined sets of surface and intracellular proteins in individual cells, using antibodies tagged with either fluorochromes (flow …
What are the difficulties with K Nearest Neighbor algorithm?
The k-nearest neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. It’s easy to implement and understand, but has a major drawback of becoming significantly slows as the size of that data in use grows.
Is the K NN classifier in high dimensions affected by the curse of dimensionality?
There is an increasing body of evidence suggesting that exact nearest neighbour search in high-dimensional spaces is affected by the curse of dimensionality at a fundamental level. However, the performance of the classifier in very high dimensions is provably unstable.