Similarity between Euclidean and cosine angle distance for nearest neighbor queries

Qian, Gang; Sural, Shamik; Gu, Yuelong; Pramanik, Sakti

doi:10.1145/967900.968151

Cited by 213 publications

(105 citation statements)

References 15 publications

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“…This can happen because as the number of dimensions grows significantly, ESM (Euclidian Similarity Measure) and CSM (Cosine Similarity Measure) eventually become less similar. In very high dimensional spaces as dimension gets higher (≥ 128) [19], the two similarity measures start having small variations between them. However, the rate of decrease of similarity is very slow.…”

Section: Discussionmentioning

confidence: 99%

Effective Hybrid Recommendation Combining Users-Searches Correlations Using Tensors

Rawat

Nayak

2011

Web Technologies and Applications

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Abstract. Most recommendation methods employ item-item similarity measures or use ratings data to generate recommendations. These methods use traditional two dimensional models to find inter relationships between alike users and products. This paper proposes a novel recommendation method using the multi-dimensional model, tensor, to group similar users based on common search behaviour, and then finding associations within such groups for making effective inter group recommendations. Web log data is multi-dimensional data. Unlike vector based methods, tensors have the ability to highly correlate and find latent relationships between such similar instances, consisting of users and searches. Non redundant rules from such associations of user-searches are then used for making recommendations to the users.

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Section: Discussionmentioning

confidence: 99%

Effective Hybrid Recommendation Combining Users-Searches Correlations Using Tensors

Rawat

Nayak

2011

Web Technologies and Applications

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“…For instance, indexes like [5] could be exploited for speeding up the computation of naming functions.…”

Section: Discussionmentioning

confidence: 99%

Naming Functions for the Vector Space Model

Tzitzikas

Theoharis

2007

Lecture Notes in Computer Science

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Abstract. The Vector Space Model (VSM) is probably the most widely used model for retrieving information from text collections (and recently from over other kinds of corpi). Assuming this model, we study the problem of finding the best query that "names" (or describes) a given (unordered or ordered) set of objects. We formulate several variations of this problem and we provide methods and algorithms for solving them.

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“…Calculate Euclidean Distance: Classification will be performed by comparing the feature vectors of the training images with feature vector of input testing image. Distance measure is an important part of a vector model [Qian, 2004]. It can be done by using the distance measures; Euclidean Distance is probably the most widely used distance metric.…”

Section: Process Recognitionmentioning

confidence: 99%

Eigenface Based Recognition of Emotion Variant Faces

Thuseethan

Kuhanesan

2016

SSRN Journal

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In present, the automatic machine based face recognition has become significant due to its urgency in potential application and current scientific challenges of industries. However, most of the existing systems designed up to now can only effectively distinguish the faces when source images are collected under numerous constrained conditions. The success rate or positive impact of face recognition systems depend on a variety of information imposed in images of human faces. Pose of face, facial expression, angle, occlusion and state of structural components are some of those. Emotions can be expressed in different ways that can be seen such as facial expression, speech, written text and gestures. This model propose an efficient approach for the recognition of expression or emotion variant faces since there are very few emotion recognition software tools to handle such problems and there is a significant importance to this research area in the field of face recognition. Especially an approach proposed here to face recognition where the facial expression in the training image set and in the testing image set diverge and only one sample image per class is existing in the system. The input to the system is a frontal neutral expression oriented face image with unique background. In this image the hair is tied away from the face and facial hair should be removed. Principal Component Analysis approach was used as a primary mechanism in the proposed model. This approach has been applied purely on a set of face images in order to extract a set of eigenface images as the output. Here weights of the representation or image are used for recognition of emotions. One of the distance metric approaches Euclidean Distance used to discover the distance with the weight vectors which was associated with each of the training images for the existence of classification task.

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Similarity between Euclidean and cosine angle distance for nearest neighbor queries

Cited by 213 publications

References 15 publications

Effective Hybrid Recommendation Combining Users-Searches Correlations Using Tensors

Effective Hybrid Recommendation Combining Users-Searches Correlations Using Tensors

Naming Functions for the Vector Space Model

Eigenface Based Recognition of Emotion Variant Faces

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