Nearest neighbour approach in the least-squares data imputation algorithms

Wasito, Ito; Mirkin, Boris

doi:10.1016/j.ins.2004.02.014

Cited by 56 publications

(32 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Analyzing data with missing values has attracted extensive research interest recently [7,[14][15][16]. The existing work The probability of τ to occur δ…”

Section: Related Workmentioning

confidence: 99%

Searching Dimension Incomplete Databases

Wei

Jin

Sun

et al. 2014

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

Abstract-Similarity query is a fundamental problem in database, data mining and information retrieval research. Recently, querying incomplete data has attracted extensive attention as it poses new challenges to traditional querying techniques. The existing work on querying incomplete data addresses the problem where the data values on certain dimensions are unknown. However, in many reallife applications, such as data collected by a sensor network in a noisy environment, not only the data values but also the dimension information may be missing. In this work, we propose to investigate the problem of similarity search on dimension incomplete data. A probabilistic framework is developed to model this problem so that the users can find objects in the database that are similar to the query with probability guarantee. Missing dimension information poses great computational challenge, since all possible combinations of missing dimensions need to be examined when evaluating the similarity between the query and the data objects. We develop the lower and upper bounds of the probability that a data object is similar to the query. These bounds enable efficient filtering of irrelevant data objects without explicitly examining all missing dimension combinations. A probability triangle inequality is also employed to further prune the search space and speed up the query process. The proposed probabilistic framework and techniques can be applied to both whole and subsequence queries. Extensive experimental results on real-life data sets demonstrate the effectiveness and efficiency of our approach.

show abstract

“…Analyzing data with missing values has attracted extensive research interest recently [7,[14][15][16]. The existing work The probability of τ to occur δ…”

Section: Related Workmentioning

confidence: 99%

Searching Dimension Incomplete Databases

Wei

Jin

Sun

et al. 2014

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

show abstract

“…This enables us to extend the basic iteration with the following normalization step: First set α = ∥v ℓ+1 ∥ 2 , then u ℓ+1 and v ℓ+1 are replaced with αu ℓ+1 and v ℓ+1 /α, respectively, e.g., [276].…”

Section: Computing a Rank-one Approximation: The "Criss-cross" Iterationmentioning

confidence: 99%

“…. .. A rank-one iterative SVD computes a dominant pair of singular vectors of A i by applying the Power iteration (8.6) on A i , e.g., [146] or [276].…”

Section: Computing a Rank-one Approximation: The "Criss-cross" Iterationmentioning

confidence: 99%

Imputing Missing Entries of a Data Matrix: A Review

Dax¹

2014

JAC

View full text Add to dashboard Cite

The problem of imputing missing entries of a data matrix is easy to state: Some entries of the matrix are unknown and we want to assign "appropriate values" to these entries. The need for solving such problems arises in several applications, ranging from traditional ields to modern ones. Typical examples of traditional ields are statistical analysis of incomplete survey data, business reports, operations management, psychometrika, meteorology and hydrology. Modern applications arise in machine learning, data mining, DNA microarrays data, chemometrics, computer vision, recommender systems and collaborative iltering. The problem is highly interesting and challenging. Many ingenious algorithms have been proposed, and there is vast literature on imputing techniques. Yet, most of the papers consider the imputing problem within the context of a speci ic application or a speci ic approach. The current survey provides a broad view of the problem, one that exposes the large variety of imputing methods. Old and new methods are examined with focus on the ideas behind the methods. It is illustrated how simple ideas evolve into highly sophisticated algorithms. The review enables us to identify a number of basic concepts and tools which are shared by several imputing methods. Equivalence theorems that we prove reveal surprising relations between apparently different methods.

show abstract

“…If the missing semantic trait is highly correlated with another trait then it is beneficial to exploit this relationship to predict the missing term. The technique uses a similar method as the k nearest neighbour (kNN) classification technique and was inspired by work within [13]. Each subject within the training set is compared to the subject containing the missing value.…”

Section: Missing Semantic Labelsmentioning

confidence: 99%

Imputing human descriptions in semantic biometrics

Reid

Nixon

2010

Proceedings of the 2nd ACM Workshop on Multimedia in Forensics, Security and Intelligence

View full text Add to dashboard Cite

Human identification at a distance has received significant interest due to the ever increasing surveillance infrastructure. Biometrics such as face and gait offer a suitable physical attribute to uniquely identify people from a distance. When linking this with human perception, these biometrics suffer from the semantic gap which is the difference between how people and how biometrics represent and describe humans. Semantic biometrics bridges this gap, allowing conversions between gait biometrics and semantic descriptions. One possible application of semantic biometrics is to automatically search surveillance footage for a person who best matches a given semantic description -possibly obtained from an eyewitness report. We now exploit patterns and structure within the physical descriptions to be able to predict occluded or erroneous data, thereby widening application potential. We show how imputation techniques can be used to increase accuracy and robustness of automatic semantic annotation of gait signatures.

show abstract

Nearest neighbour approach in the least-squares data imputation algorithms

Cited by 56 publications

References 27 publications

Searching Dimension Incomplete Databases

Searching Dimension Incomplete Databases

Imputing Missing Entries of a Data Matrix: A Review

Imputing human descriptions in semantic biometrics

Contact Info

Product

Resources

About