lecture 2 - handling sensory noise

Question 1

definition: outlier

Answer 1

an observation that is distant from other observations

Question 2

possible causes for outliers

Answer 2

1. measurement error
2. variability in the data

Question 3

outlier removal methods

Answer 3

1. using domain knowledge (e.g., heart rate cant be over 220)
2. outlier detection methods

outlying values are replaced

Question 4

two types of outlier detection

Answer 4

1. distribution based: cauvenet’s criterion and mixture models
2. distance based: simple, LOF

Question 5

chauvenet’s criterion: purpose + steps

Answer 5

• purpose: to identify values of an attribute that are unlikely, given a normal distribution
• The criterion essentially states that an observation is an outlier if it is so extreme that its probability of occurrence is less than 1/N
• The higher N the less strict Chauvenet’s criterion is.

1. assume a normal distribution for a single attribute/feature
2. take mean and standard deviation as parameters for that normal distribution
3. for each instance of the attribute, compute the probability of the observation
4. define the instance as an outlier when it’s probability is smaller than chauvenet’s criterion

Question 6

mixture models

Answer 6

• assuming the data of an attribute to follow a single distribution might be too simple
• therefore, we describe the data with k normal distributions, where pi weights each distribution
• we find the parameters (mu, sigma) and combinations of weights (pi) that best describe the data by maximizing the likelihood

Question 7

mixture models: pi

Answer 7

• weights of the distributions
• all weights together must sum to 1

Question 8

mixture models: likelihood maximization

Answer 8

• finding parameters pi, mu, and sigma for each distribution
• iteratively improves the parameter estimates to maximize the likelihood
• multiplies all probabilities of each data point under the distribution.

Question 9

simple distance-based approach

Answer 9

1. points x and y are close when their distance is within d_{min} (smaller d_{min} = more strict)
2. points are outliers when they are outside of d_{min} and (number of points outside of d_{min})/N is bigger than fraction f_{min}

• does not take local density into account

Question 10

LOF properties

Answer 10

• takes local density into account
• consider k closest data points
• computationally expensive

Question 11

LOF steps

Answer 11

1. define k_{dist} for a point A as the distance to its k-nearest neighbor (furthest neigbor)
2. the set of neighbours within k_{dist} is called the k-distance neighbourhood
3. define the reachability distance for point A to B as max(k_dist(B), d(A, B))
4. define local reachability distance of our point (inverse of the average reachability distance between A and all other points in the neighborhood)
5. compare this to the neighbours local reachability distances to get the local outlier factor

Question 12

k_{dist}

Answer 12

distance of a point to its k-nearest neighbor

• i.e., the furthest neighbor
• k_{dist} is small when a point is in a dense region

Question 13

k-distance neighbourhood

Answer 13

the set of data points that have a distance to x^j_i smaller than k_{dist}

Question 14

reachability distance

Answer 14

• the distance between point A and B
• the maximum of [the distance between point A and B] and [the k_{dist} of point B]
• this distance is k_{dist}(B) if A is inside of the neighbourhood of B, and the actual distance if it is outside of the neighbourhood of B
• ensures that smaller distances within the k-distance neighborhood are not overemphasized

Question 15

local reachability distance

Answer 15

1/ ([sum of reachability distances from A to all points in the neighbourhood] / [number of points in the neighbourhood])

• inverse of the average reachability
• small value = likely an outlier
• range [0,1]

Question 16

LOF formula

Answer 16

(sum of (lrd of an x / lrd of x^j_i)) / number of points in neighbourhood

• tells you how much of an outlier this point is compared to its neighbourhood

Question 17

imputation

Answer 17

1. replace missing values by a substituted value (e.g., mean, mode, median)
2. more advanced:
   - exploit information from other attributes in the same instance (row)
   - values of the same attributes from other instances (column).
   –> singe vs multiple missing values

Question 18

exploit information o the same attribute from other instances: single missing value vs multiple missing values

Answer 18

• single missing measurement: take the average of the instance before and after the missing value
• multiple missing measurements: linear imputation

Question 19

kalman filter

Answer 19

estimates expected values based on historical data with:

1. prediction
2. measurement
3. updating

if the observed value deviates too much, we can impute it with the expected value

Question 20

kalman filter steps

Answer 20

1. measure state s_t with x_t
2. state prediction: predict the next state (s-hat_{t|t-1} with transition matrix F and the previous state
3. predict error covariance matrix (P_{t|t-1}) as the difference between the actual state and the predicted state
4. updated state based on error (s-hat_{t|t} = prediction + kalman gain * error covariance

Question 21

data transformation

Answer 21

1. lowpass filter
2. PCA

Question 22

lowpass filter

Answer 22

• for periodic data
• series of values is decomposed into different periodic signals that come with their own frequencies
• filters out irrelevant frequencies by filtering out high frequency data

Question 23

PCA

Answer 23

• find new features that explain most of the variability in our data
• select the number of components based on the explained variance

Question 24

temporal specific outlier/imputation methods

Answer 24

1. interpolation based imputation
2. kalman filter
3. lowpass filter

Question 25

lowpass filter parameters

Answer 25

- f_c = cutoff frequency - n = how quickly something disappears from your data if it exceeds the cutoff frequency --> i.e., higher n = more filtering out of that frequency

Question 26

lowpass filter magnitude

Answer 26

- high value of f = lower magnitude of the filter = frequency is not forwarded - low value of f = high magnitude of the filter = frequency is kept

Question 27

mean imputation

Answer 27

numeric features

Question 28

mode imputation

Answer 28

categorical and numeric features

Question 29

median imputation

Answer 29

numeric features