Outlier preservation by dimensionality reduction techniques
نویسندگان
چکیده
منابع مشابه
Outlier preservation by dimensionality reduction techniques
Sensors are increasingly part of our daily lives: motion detection, lighting control, and energy consumption all rely on sensors. Combining this information into, for instance, simple and comprehensive graphs can be quite challenging. Dimensionality reduction is often used to address this problem, by decreasing the number of variables in the data and looking for shorter representations. However...
متن کاملDimensionality Reduction with Subspace Structure Preservation
Modeling data as being sampled from a union of independent subspaces has been widely applied to a number of real world applications. However, dimensionality reduction approaches that theoretically preserve this independence assumption have not been well studied. Our key contribution is to show that 2K projection vectors are sufficient for the independence preservation of any K class data sample...
متن کاملImage Reduction Using Assorted Dimensionality Reduction Techniques
Dimensionality reduction is the mapping of data from a high dimensional space to a lower dimension space such that the result obtained by analyzing the reduced dataset is a good approximation to the result obtained by analyzing the original data set. There are several dimensionality reduction approaches which include Random Projections, Principal Component Analysis, the Variance approach, LSA-T...
متن کاملTransformation invariant and outlier revealing dimensionality reduction using triplet embedding
ABSTRACT We propose a number of tests for dimensionality reduction methods. The tests allow the practitioner to build con�dence about the dimensionality reduction results of a particular method. The tests include the invariance of the dimensionality reduction method to certain natural transformations and the detection of constructed outliers. We then evaluate the most commonly used dimensionali...
متن کاملOutlier Detection in the Framework of Dimensionality Reduction
We propose an effective outlier detection algorithm for high-dimensional data. We consider manifold models of data as is typically assumed in dimensionality reduction/manifold learning. Namely, we consider a noisy data set sampled from a low-dimensional manifold in a high-dimensional data space. Our algorithm uses local geometric structure to determine inliers, from which the outliers are ident...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Data Analysis Techniques and Strategies
سال: 2015
ISSN: 1755-8050,1755-8069
DOI: 10.1504/ijdats.2015.071365