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Category: Pivot selection strategies for permutation

Pivot selection strategies for permutation

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Recently, permutation based indexes have attracted interest in the area of similarity search. The basic idea of permutation based indexes is that data objects are represented as appropriately generated permutations of a set of pivots or reference objects.

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Similarity queries are executed by searching for data objects whose permutation representation is similar to that of the query, following the assumption that similar objects are represented by sim-ilar permutations of the pivots.

In the context of permutation-based indexing, most authors propose to select pivots randomly from the data set, given that traditional pivot selection techniques do not reveal better performance. However, to the best of our knowledge, no rigorous comparison has been performed yet.

In this paper we compare five pivot selection techniques on three permutation-based similarity access methods. Among those, we propose a novel technique specifically designed for permutations.

Two significant observations emerge from our tests. First, random selection is always outperformed by at least one of the tested techniques. Second, there is not a technique that is universally the best for all permutation-based access methods; rather different techniques are optimal for different methods.

This indicates that the pivot selection technique should be considered as an integrating and relevant part of any permutation-based access method. Documents: Advanced Search Include Citations. Abstract Recently, permutation based indexes have attracted interest in the area of similarity search.

6. experimental results and discussion

Powered by:.Recently, permutation based indexes have attracted interest in the area of similarity search. The basic idea of permutation based indexes is that data objects are represented as appropriately generated permutations of a set of pivots or reference objects.

Similarity queries are executed by searching for data objects whose permutation representation is similar to that of the query. This, of course assumes that similar objects are represented by similar permutations of the pivots. In the context of permutation-based indexing, most authors propose to select pivots randomly from the data set, given that traditional pivot selection strategies do not reveal better performance.

However, to the best of our knowledge, no rigorous comparison has been performed yet. In this paper we compare five pivots selection strategies on three permutation-based similarity access methods.

Among those, we propose a novel strategy specifically designed for permutations. Two significant observations emerge from our tests.

First, random selection is always outperformed by at least one of the tested strategies. Second, there is not a strategy that is universally the best for all permutation-based access methods; rather different strategies are optimal for different methods. Unable to display preview. Download preview PDF. Skip to main content. This service is more advanced with JavaScript available.

Advertisement Hide. International Conference on Similarity Search and Applications. Conference paper. This is a preview of subscription content, log in to check access. Amato, G. Batko, M. Bolettieri, P. Brin, S. Morgan Kaufmann Google Scholar. Bustos, B.Proximity searching consists in retrieving objects out of a database similar to a given query. Nowadays, when multimedia databases are growing up, this is an elementary task. The permutation based index PBI and its variants are excellent techniques to solve proximity searching in high dimensional spaces, however they have been surmountable in low dimensional ones.

In the following, we introduce an improvement on the PBI that allows to produce a better promissory order using less space than the basic permutation technique and also gives us information to discard some elements. To do so, besides the permutations, we quantize distance information by defining distance rings around each permutant, and we also keep this data.

The experimental evaluation shows we can dramatically improve upon specialized techniques in low dimensional spaces. Nowadays, similarity searching has become an important task for retrieving objects in a multimedia database; with applications in pattern recognition, data mining and computational biology, to name a few.

This task can be mapped into a metric space problem. The last one being useful to discard objects when solving similarity queries. There are some indices to speed up similarity queries.

One of them, the permutation-based index PBI [ 3 ] has a competitive performance in the hard case of high dimensional spaces. Oddly, in low dimensional spaces, this technique has poor performance when compared with, for instance, the pivot-based index which is particularly well suited for low dimensional spaces, as reported in [ 4 ].

Another important drawback of the PBI is that it does not allow to discard objects when solving similarity queries. Our contribution consists in granting the basic PBI the capability of safely discard objects. For this sake, we enhance the PBI with distance information in a convenient way, so that the increment of the space requirement is very small.

Our technique allows to improve the retrieval of the PBI in low and medium dimensional metric spaces in a dramatic way.

pivot selection strategies for permutation

There are three kinds of indices for proximity searching in metric spaces, namely, pivot-based indices, partition-based indices and permutation-based indices. There are many techniques based on this idea.

Some of them try to reduce the memory requirements in order to get a small index, and others to reduce the number of distance evaluations. These kinds of techniques have a competitive performance in low dimensional spaces. One can imagine that a low dimensional space is one that can be embedded in a uniformly distributed vector space whose dimension is lower than 16, preserving the relative distances among objects.

Among the pivoting techniques, AESA [ 10 ] excels in the searching performance. To do this, AESA considers that every object could be a potential pivot. So, it stores the whole matrix of distances between every object pair. Since every object can operate as a pivot, the authors also define a scheme to sequencing the pivot selection.

So, the first pivot is an object chosen at random, and from the second pivot, AESA uses the non-discarded object that minimize SumLB u. AESA is the bottom line of exact algorithms.

Partition-based indices split the space into zones as compact as possible and assign the objects to these zones. These indices have a competitive performance in high dimensional spaces.

Pivot Selection Strategies for Permutation-Based Similarity Search

Permutants at the same distance take an arbitrary but consistent order. Thus, a simple implementation needs nm space. Nevertheless, as only the permutant identifiers are necessary, several identifiers can be compacted in a single machine word. The hypothesis of the PBI is that two equal objects are associated to the same permutation, while similar objects are, hopefully, related to similar permutations.

Regrettably, PBI cannot discard objects at query time. Instead, a premature cut off in the reviewing process produces a probabilistic search algorithm as it reports the right answer to the query with some probability. There are some techniques to select good permutants obtaining little improvement [ 17 ]. A simple way to reduce the memory requirement when representing the distances among objects is to quantize them.

Of course, there is a tradeoff between memory requirement and precision. However, there are some cases where the quantization is effective.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. When implementing Quicksort, one of the things you have to do is to choose a pivot.

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But when I look at pseudocode like the one below, it is not clear how I should choose the pivot. First element of list? Something else? Can someone help me grasp the concept of choosing a pivot and whether or not different scenarios call for different strategies. Choosing a random pivot minimizes the chance that you will encounter worst-case O n 2 performance always choosing first or last would cause worst-case performance for nearly-sorted or nearly-reverse-sorted data.

Choosing the middle element would also be acceptable in the majority of cases. Also, if you are implementing this yourself, there are versions of the algorithm that work in-place i. It depends on your requirements.

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Beware of relative performance of comparisons, though; if your comparisons are costly, then Mo3 does more comparisons than choosing a single pivot value at random. Database records can be costly to compare. Choose three random indexes, and take the middle value of this.

The whole point is to make sure that your choice of pivots is not deterministic - if it is, worst case data can be quite easily generated.

There's an article described at portal. Section 2 'The Algorithm' begins: ' By using the median of the first, middle and last elements of A[L:R], efficient partitions into parts of fairly equal sizes can be achieved in most practical situations. Another short article that is interesting is by M. It explains how to make almost any Quicksort behave quadratically. Sedgewick [ This indicates that both techniques for 'median-of-three' are known in the literature.

Update The article appears to be available at IEEE Xplore or from Wiley — if you have membership or are prepared to pay a fee. There is a lot of discussion of trade-offs for various approaches. There are several options. Simple: Pick the first or last element of the range. However, picking any arbitrary element runs the risk of poorly partitioning the array of size n into two arrays of size 1 and n For most data, picking the first or last is sufficient.

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But, if you find that you're running into worst case scenarios often partially sorted inputthe first option would be to pick the central value Which is a statistically good pivot for partially sorted data.

Never ever choose a fixed pivot - this can be attacked to exploit your algorithm's worst case O n 2 runtime, which is just asking for trouble. Quicksort's worst case runtime occurs when partitioning results in one array of 1 element, and one array of n-1 elements. Suppose you choose the first element as your partition. If someone feeds an array to your algorithm that is in decreasing order, your first pivot will be the biggest, so everything else in the array will move to the left of it.

Then when you recurse, the first element will be the biggest again, so once more you put everything to the left of it, and so on. A better technique is the median-of-3 methodwhere you pick three elements at random, and choose the middle.

You know that the element that you choose won't be the the first or the last, but also, by the central limit theorem, the distribution of the middle element will be normal, which means that you will tend towards the middle and hence, nlog n time.

If you absolutely want to guarantee O nlog n runtime for the algorithm, the columns-of-5 method for finding the median of an array runs in O n time, which means that the recurrence equation for quicksort in the worst case will be:.Recently, permutation based indexes have attracted interest in the area of similarity search.

The basic idea of permutation based indexes is that data objects are represented as appropriately generated permutations of a set of pivots or reference objects. Similarity queries are executed by searching for data objects whose permutation represen-tation is similar to that of the query.

This, of course assumes that similar objects are represented by similar permutations of the pivots. In the context of permutation-based indexing, most authors propose to select pivots randomly from the data set, given that traditional pivot se-lection strategies do not reveal better performance.

pivot selection strategies for permutation

However, to the best of our knowledge, no rigorous comparison has been performed yet. In this paper we compare five pivots selection strategies on three permutation-based similarity access methods. Among those, we propose a novel strat-egy specifically designed for permutations. Two significant observations emerge from our tests. First, random selection is always outperformed by at least one of the tested strategies.

Second, there is not a strategy that is universally the best for all permutation-based access methods; rather different strategies are optimal for different methods.

pivot selection strategies for permutation

Documents: Advanced Search Include Citations. Abstract Abstract. Powered by:.The activation of the Deep Convolutional Neural Networks hidden layers can be successfully used as features, often referred as Deep Features, in generic visual similarity search tasks. Recently scientists have shown that permutation-based methods offer very good performance in indexing and supporting approximate similarity search on large database of objects.

Permutation-based approaches represent metric objects as sequences permutations of reference objects, chosen from a predefined set of data.

However, associating objects with permutations might have a high cost due to the distance calculation between the data objects and the reference objects. In this work, we propose a new approach to generate permutations at a very low computational cost, when objects to be indexed are Deep Features. We show that the permutations generated using the proposed method are more effective than those obtained using pivot selection criteria specifically developed for permutation-based methods.

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Conference paper First Online: 27 September This is a preview of subscription content, log in to check access.

Boosting the Permutation Based Index for Proximity Searching

Amato, G. In: Madria, S.

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DaWaK LNCS, vol. Springer, Heidelberg In: Brisaboa, N. SISAP Multimedia Tools Appl. Azizpour, H.

Deep Permutations: Deep Convolutional Neural Networks and Permutation-Based Indexing

Babenko, A. In: Fleet, D. Chandrasekhar, V. IEEE Trans.The idea is to generate each permutation from the previous permutation by choosing a pair of elements to interchange, without disturbing the other n-2 elements. Following is the illustration of generating all the permutations of n given numbers. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.

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pivot selection strategies for permutation

Attention reader! Writing code in comment? Please use ide. This will generate all of the permutations that end with the last element. If n is odd, swap the first and last element and if n is even, then swap the i th element i is the counter starting from 0 and the last element and repeat the above algorithm till i is less than n.

In each iteration, the algorithm will produce all the permutations that end with the current last element. Python program to print all permutations using.

Permutations and Combinations - Selection - Don't Memorise - GMAT/CAT/Bank PO/SSC CGL

Generating permutation using Heap Algorithm. WriteLine. Length, a. Length. Load Comments. We use cookies to ensure you have the best browsing experience on our website.


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